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BACKGROUND: T(2)-weighted, two-point Dixon fast-spin-echo (FSE) is an effective technique for magnetic resonance neurography (MRN) that can provide quantitative assessment of muscle denervation. Low signal-to-noise ratio and inadequate fat suppression, however, can impede accurate interpretation. PURPOSE: To quantify effects of principal component analysis (PCA) denoising on tissue signal intensities and fat fraction (FF) and to determine qualitative image quality improvements from both denoising and water-weighting (WW) algorithms to improve nerve conspicuity and fat suppression. STUDY TYPE: Prospective. SUBJECTS: Twenty-one subjects undergoing MR neurography evaluation (11/10 male/female, mean age=46.3+/−13.7 years) with 60 image volumes. Twelve subjects (23 image volumes) were determined to have muscle denervation based on diffusely elevated T(2) signal intensity. FIELD STRENGTH/SEQUENCE: 3T, 2D, two-point Dixon FSE. ASSESSMENT: Qualitative assessment included overall image quality, nerve conspicuity, fat suppression, pulsation and ringing artifacts by 3 radiologists separately on a three-point scale (1=poor, 2=average, 3=excellent). Quantitative measurements for FF and signal intensity relative to normal muscle were made for nerve, abnormal muscle and subcutaneous fat. STATISTICAL TESTS: Linear and ordinal regression models were used for quantitative and qualitative comparisons, respectively; 95% confidence intervals (CIs) and p-values for pairwise comparisons were adjusted using the Holm-Bonferroni method. Inter-rater agreement was assessed using Gwet’s agreement coefficient (AC(2)). RESULTS: Simulations showed PCA-denoising reduced FF error from 2.0% to 1.0%, and from 7.6% to 3.1% at noise levels of 10% and 30%, respectively. In human subjects, PCA-denoising did not change signal levels and FF quantitatively. WW decreased fat signal significantly (−83.6%, p

The image compression-decompression process causes image quality degradations such as blocking and ringing artifacts. A convolutional neural network based on DCT domain is proposed to learn the mapping relationship between JPEG images and original images to reduce compression artifacts in this work. The convolutional neural network can exploit the prior knowledge of JPEG compression in DCT domain. The compression artifacts reduction network which is proposed in this work has three advantages. First, it can expand the receptive field and eliminate the discontinuity between each 8 × 8 block by overlapping extraction of patches. Second, it is based on the essence of distortion and can predict true DCT coefficients more accurately to reduce the loss of JPEG images. Third, it can obviously reduce the compression artifacts in JPEG images. Experiments on compressed images demonstrate that our approach achieves state-of-the-art performance in both the objective parameters and the subjective visual quality.

Medical image fusion involves combining multiple images of diverse modalities to acquire superior image quality while retaining the information of an individual image. The fusion process aids physicians to diagnose and assess the disease by increasing the visual information and clarity. Direct fusion methods often generate undesirable effects leading to distortion and poor contrast. To some extent, multi-scale decomposition (MSD) methods have achieved success in various image fusion problems. However, they suffer from ringing artifacts, due to the blur caused by strong edges in decomposition steps. Hence, to overcome these drawbacks, we propose an efficient and novel image fusion algorithm for constructing a fused image through total variation (TV-L1) model using an optimized adaptive weighting scheme. Our method focuses on transferring the prominent textural details of Magnetic Resonance Imaging (MRI) and visual details of Positron Emission Tomography (PET) by cartoon and texture decomposition. It consists of data fidelity term to force the cartoon component to remain close to original image and a regularization term to transfer gradients from original image to the fused image. The present work preserves structural and functional details with high contrast. Experiments were conducted using MRI and PET images collected from standard Brain Atlas Datasets. The qualitative and quantitative analysis of the work suggest that the proposed method achieves better quality and accuracy as compared with the available state-of-the-art techniques. Keywords: TV-L1, Particle swarm optimization, Image fusion, MRI, PET

Image deblurring is a fundamental problem in image processing. When the blur type is unknown, the problem becomes more challenging, which is called blind deblurring. In this paper, we propose a novel variational method for single image blind deblurring based on the fractional-order differential, which can overcome the staircase effect produced by the total variation regularization and alleviate the ringing artifact in deblurring. The fractional-order gradient fidelity term is added in the cost functional to improve the restoration. Moreover, we make use of the edge detect function with the fractional-order gradient to preserve sharp edges, and the Bregman iteration to reconstruct more structures. The primal–dual algorithm is developed to solve the proposed model. Numerical experiments show that our method is able to get the sharp image without the staircase effect and correctly estimate the unknown blur kernel.

Compressed sensing-based reconstruction (CSR) is a new magnetic resonance (MR) image reconstruction method based on the compressed sensing (CS) technique. CSR suppresses ringing artifacts from truncated k-space sampling by estimating the high spatial frequency information required to support the acquired k-space data. CSR is intended to replace the existing zero-fill interpolation (ZIP) reconstruction. We investigated the usefulness of the CSR technique by obtaining sagittal T2-weighted images of the cervical spine and phantom images using CSR or ZIP. Our results indicated that the CSR technique reduces truncation artifacts compared to ZIP without prolonging the scan time or impairing image sharpness.

A computational light-field display system is designed to realize floating three-dimensional (3D) visual effect. To simulate the natural vision of 3D scene, a multi-rays computational encryption method is proposed to obtain the image which is displayed on the LCD. In the process of encrypting image, N×N light rays are evenly involved in optimization to obtain the value of a pixel. The quality of reconstructed 3D image is correlated to N. As the value of N increases, the sharpness is improved and the ringing artifacts are eliminated. Here, the proposed computational light-field display system based on holographic functional screen (HFS) can provide floating 3D visual effect to observers with high quality. The constructed 3D image has continuously varying viewpoints with 40° viewing angle in horizontal and vertical directions. The displayed depth is more man 20 cm, and the center of the 3D object is on the surface of HFS.

Blind image deconvolution is a highly ill-posed problem. As a generalization of the well known Weiner filter, the existing iterative Weiner filter (IWF) method for blind image deconvolution is unstable and suffers from serious ringing artifacts. To overcome these drawbacks, in this paper, we propose two novel regularized iterative Weiner filter methods. We assume that both the latent image and the convolution kernel can be estimated by applying two different filters on the observed image. To estimate the filters, we propose to minimize energy functionals combined by the mean square errors with some regularization terms. Both H 1 and total variation (TV) regularization are considered. By applying alternating minimization method and operator splitting technique, we derive iterative algorithms for each regularization method. The proposed methods are effective for blind deconvolution of Gaussian blurred images which is widely observed in real applications such as microscopic images. Numerical experimental results on both synthetic images and real microscopic images are presented. The comparisons show that the proposed regularized algorithms perform better than the closely related state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR) and visual quality.

We proposed a single image motion deblurring method with reduced ringing effects using variational Bayesian estimation.We propose a method to reduce ringing for image restoration using cyclic boundaries.Our algorithm can effectively suppress ringing artifacts and eliminate the influence of noise while protecting edge and detail information. Image degradation commonly occurs when camera translation or irregular movement causes motion blur. In this paper, we remove the effects of unknown camera shake from an image using variational Bayesian estimation and the statistical characteristics of natural image gradients. We then propose a method to reduce ringing effects for image restoration that uses cyclic boundaries. Finally, we use the Richardson-Lucy algorithm to restore an image. Experimental results demonstrate that the blind restoration algorithm that is proposed in this paper can effectively remove image blurring caused by camera shake. Our algorithm can effectively suppress ringing artifacts and eliminate the effects of noise while protecting both edge and detail information. In general, the proposed motion deblurring method is effective in reducing both noise and ringing effects to enhance the quality of motion-blurred image restoration, and we obtained restored images that were clearer and more reliable.

Camera shake during the exposure time often leads to spatially varying blurring effect on images. Existing work usually uses patch based methods that assume the blur in each patch is uniform to solve this problem. However, these kinds of methods do not consider the consistency between different patches and thus leading to inaccurate results with ringing artifacts. In this paper, we propose a Kernel mapping regularized method to solve the non-uniform deblurring problem, where the consistency between image patches is considered to improve blur Kernel estimation. We analyze the theoretical framework of blur Kernels which can be described as a motion path transference, and propose a robust Kernel estimation algorithm based on Earth mover’s distance (Wasserstein metric) to preserve the properties of blur Kernels. In addition, we develop a new Kernel refinement method based on a proposed Ink Dot Diffusion that uses 8 directions of Kernel mapping flow where the erroneous Kernels are identified and corrected. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art image deblurring methods.

Nonlocal image representation has been successfully used in many image-related inverse problems including denoising, deblurring and deblocking. However, most existing methods only consider the nonlocal self-similarity (NSS) prior of degraded observation image, and few methods use the NSS prior from natural images. In this paper we propose a novel method for image denoising via group sparsity residual constraint with external NSS prior (GSRC-ENSS). Different from the previous NSS prior-based denoising methods, two kinds of NSS prior (e.g., NSS priors of noisy image and natural images) are used for image denoising. In particular, to enhance the performance of image denoising, the group sparsity residual is proposed, and thus the problem of image denoising is translated into reducing the group sparsity residual. Because the groups contain a large amount of NSS information of natural images, to reduce the group sparsity residual, we obtain a good estimation of the group sparse coefficients of the original image by the external NSS prior based on Gaussian Mixture Model (GMM) learning, and the group sparse coefficients of noisy image are used to approximate the estimation. To combine these two NSS priors better, an effective iterative shrinkage algorithm is developed to solve the proposed GSRC-ENSS model. Experimental results demonstrate that the proposed GSRC-ENSS not only outperforms several state-of-the-art methods, but also delivers the best qualitative denoising results with finer details and less ringing artifacts.

The photonics integrated interference imaging system (PIIIS) is a recently developed far-field imaging approach aimed at compacting structures and acquiring high imaging performance. In this regard, to acquire higher imaging quality, several lens patterns have been developed, providing low-frequency intensive but high-frequency sparse measurements of the visibility function. However, sparse measurements at high frequency produce Gibbs-ringing artifacts in the image reconstruction, severely damaging the PIIIS imaging quality. In this study, a new data penalty for revised entropy is proposed for PIIIS, and a simple reconstruction algorithm using the Newton method is developed. A reconstruction experiment adopting the hierarchical multistage pattern is implemented on resolution board (USAF1951) targets with bright and black backgrounds. The outstanding improvement in the experimental results proves the feasibility of suppressing the ringing artifacts in the images reconstructed in PIIIS.

The modulation transfer function (MTF) is a well-established in imaging systems and has played a significant role in assessing the performance of information transfer in accordance with the spatial frequency. Exist methods for measuring MTF require a pinhole, or a slit camera, an edge phantom, etc. These depend on the imaging system and exposure conditions. To overcome these problems, we investigated the proposed patch-based MTF prediction framework using a deep-learning algorithm in radiography. This proposed method directly allows for the predicted MTF from a single image without the need for additional devices and work. We used 800 to 1500 projections for supervised learning and selected many patches, including the edge information from input image for predicting the MTF, for which we used the Kendall's rank correlation. The quantitative evaluation performed included an intensity profile and a root-mean square error. In addition, we implemented the nonblind deblurring process to verify its image performance using the predicted MTF. Our methods successfully predicted the MTF and restored them from the degraded image without such artifacts as the ringing artifact that can occur when using the incorrect blur kernel. Consequently, the simulation and experimental results indicate that the proposed framework is useful to predicting the accurate MTF and is effective in improving radiographic image quality.

This paper proposes a blind image deconvolution method which consists of two sequential phases, i.e., blur kernel estimation and image restoration. In the first phase, we adopt the L0-norm of image gradients and total variation (TV) to regularize the latent image and blur kernel, respectively. Then we design an alternating optimization algorithm which jointly incorporates the estimation of intermediately restored image, blur kernel and regularization parameters into account. In the second phase, we propose to take the mixture of L0-norm of image gradients and TV to regularize the latent image, and design an efficient non-blind deconvolution algorithm to achieve the restored image. Experimental results on both a benchmark image dataset and real-world blurred images show that the proposed method can effectively restore image details while suppress noise and ringing artifacts, the result is of high quality which is competitive with some state of the art methods.

Digital holography has been widely applied in the measurement of complex micro–nano structures. The reconstruction of the object wavefront is usually under the assumption that the measured sample is parallel to the recording plane, which means the diffraction distance is fixed. While the tilt of the measured sample is inevitable, especially for the scanning measurement of a large component where the movement will introduce inclination. Then ringing artifacts will occur, which seriously affect the quality of the reconstructed wavefronts. In this paper, an accurate diffractive reconstruction method is proposed. A pose transformation model is established for calculating the accurate diffraction distances of the tilt wavefronts. The gradient of the reconstructed complex amplitude is utilized in the merit function for the pose estimation. By minimizing the merit function, the pose parameters can be refined with the particle swarm optimization. Experimental results demonstrate that the proposed method can effectively improve the reconstruction accuracy of the tilt wavefronts. It is significant for the scanning measurement of large components.

Purpose Single image super-resolution (SR) is highly desired in many fields but obtaining it is often technically limited in practice. The purpose of this study was to propose a simple, rapid and robust single image SR method in magnetic resonance (MR) imaging (MRI). Methods The idea is based on the mathematical formulation of the intrinsic link in k-space between a given (modulus) low-resolution (LR) image and the desired SR image. The method consists of two steps: 1) estimating the low-frequency k-space data of the desired SR image from a single LR image; 2) reconstructing the SR image using the estimated low-frequency and zero-filled high-frequency k-space data. The method was evaluated on digital phantom images, physical phantom MR images and real brain MR images, and compared with existing SR methods. Results The proposed SR method exhibited a good robustness by reaching a clearly higher PSNR (25.77dB) and SSIM (0.991) averaged over different noise levels in comparison with existing edge-guided nonlinear interpolation (EGNI) (PSNR=23.78dB, SSIM=0.983), zero-filling (ZF) (PSNR=24.09dB, SSIM=0.985) and total variation (TV) (PSNR=24.54dB, SSIM=0.987) methods while presenting the same order of computation time as the ZF method but being much faster than the EGNI or TV method. The average PSNR or SSIM over different slice images of the proposed method (PSNR=26.33 dB or SSIM=0.955) was also higher than the EGNI (PSNR=25.07dB or SSIM=0.952), ZF (PSNR=24.97dB or SSIM=0.950) and TV (PSNR=25.70dB or SSIM=0.953) methods, demonstrating its good robustness to variation in anatomical structure of the images. Meanwhile, the proposed method always produced less ringing artifacts than the ZF method, gave a clearer image than the EGNI method, and did not exhibit any blocking effect presented in the TV method. In addition, the proposed method yielded the highest spatial consistency in the inter-slice dimension among the four methods. Conclusions This study proposed a fast, robust and efficient single image SR method with high spatial consistency in the inter-slice dimension for clinical MR images by estimating the low-frequency k-space data of the desired SR image from a single spatial modulus LR image.

The purpose of this study is to improve direct phosphorus (31P) MR imaging. Therefore, 3D density-adapted radially-sampled balanced steady-state free precession (bSSFP) sequences were developed and an iterative approach exploiting additional anatomical information from hydrogen (1H) data was evaluated. Three healthy volunteers were examined at B0=7T in order to obtain the spatial distribution of the phosphocreatine (PCr) intensities in the human calf muscle with a nominal isotropic resolution of 10mm in an acquisition time of 10min. Three different bSSFP gradient schemes were investigated. The highest signal-to-noise ratio (SNR) was obtained for a scheme with two point-reflected density-adapted gradients. Furthermore, the conventional reconstruction based on a gridding algorithm was compared to an iterative method using an 1H MRI constraint in terms of a segmented binary mask, which comprises prior knowledge. The parameters of the iterative approach were optimized and evaluated by simulations featuring 31P MRI parameters. Thereby, partial volume effects as well as Gibbs ringing artifacts could be reduced. In conclusion, the iterative reconstruction of 31P bSSFP data using an 1H MRI constraint is appropriate for investigating regions where sharp tissue boundaries occur and leads to images that represent the real PCr distributions better than conventionally reconstructed images.

A new underwater video compression technique based on adaptive hybrid wavelets and directional filter banks is proposed to achieve both high coding efficiency and good reconstruction quality at very low-bit rates. A key application is the real-time transmission of video through acoustic channels with limited bandwidth, from an autonomous underwater vehicle to a surface station, e.g., for man-in-the-loop monitoring and inspection operations. For intra-frame coding, the method maintains details in texture regions at relatively low bit rates, and overcomes the ringing artifacts within smooth regions. For inter-frame coding, improved efficiency is achieved by making use of: (1) a new spatio-temporal just-noticeable-distortion model to remove perceptual redundancy; (2) motion interpolation to reduce bit rate; and (3) variable-precision in quantizing the residual error. Experiments with underwater video sequences are presented to assess the effectiveness of the proposed approach, in comparison to traditional wavelet-based techniques. HighlightsUnderwater video compression system optimized based on domain image and HVS characteristics.Improved performance over state-of-the-art DWT-based methods.Effective compression of both smooth and textured regions.New spatio-temporal just noticeable distortion (JND) model.Low bit-rate coding to meet underwater acoustic channel capacity.

A blur map estimation method using edge information is proposed.The blur map is segmented into multiple superpixels according to the image contours.Ringing artifacts and noise are detected and removed after deconvolution. In this paper, we propose a single image deblurring algorithm to remove spatially variant defocus blur based on the estimated blur map. Firstly, we estimate the blur map from a single image by utilizing the edge information and K nearest neighbors (KNN) matting interpolation. Secondly, the local kernels are derived by segmenting the blur map according to the blur amount of local regions and image contours. Thirdly, we adopt a BM3D-based non-blind deconvolution algorithm to restore the latent image. Finally, ringing artifacts and noise are detected and removed, to obtain a high quality in-focus image. Experimental results on real defocus blurred images demonstrate that our proposed algorithm outperforms some state-of-the-art approaches.

360-degree video is becoming popular with the development of the virtual reality(VR) technology in recent years. For this kind of video, pictures are projected to a two-dimensional plane. Standard video encoders are used for compression. Due to the projection mapping of the spherical video to the two-dimensional plane video, many conventional coding tools can be adjusted properly for better performance. As the 360-degree videos are high resolution videos, usually 8K resolution videos, the high complexity for encoding 360-degree videos is a problem to be solved urgently, especially for the latest video standard H.265/HEVC. Sample adaptive offset (SAO) is a new tool adopted in H.265/HEVC. It can reduce ringing artifacts and achieve higher coding efficiency. But it also introduces high computational complexity, especially on high resolution videos. In this paper, a fuzzy control based fast SAO method is proposed to reduce the high computational complexity in SAO encoding process according to the characteristics of the ERP format video. Compared with the original algorithm in the reference software HM 16.14, experimental results show that the proposed method can reduce about 73% SAO encoding time on average under the common test conditions with negligible loss.

Image feature extraction is a significant task for the computer to understand the natural environment. Infrared imaging spectrum is an efficient tool to achieve this. However, the infrared spectroscopic data often exists the problems of random noises and peaks overlap. In this paper, we propose a blind infrared spectral deconvolution with sparse representation regularization for image feature extraction. The Bandelet transform is applied on the degraded IR spectrum and clean spectrum, their distributions of the Bandelet transforms coefficients are compared. By incorporating the sparse prior to the spectral deconvolution process, we could effectively constrain the inverse problem and control the ringing artifact which is often produced by the traditional algorithms. Compared with serval state-of-the-art methods, the proposed method shows its effectiveness in the ability of noise suppression and overlap band splitting. The recovered infrared spectrum data can be used for extracting accurate image feature in COREL dataset.

In order to suppress and reduce the ringing artifacts of the IBP super resolution algorithm, an improved iterative back projection (IBP) reconstruction algorithm is presented in this paper. Firstly, two novel concepts, variation and local variance variation based on an adaptive analysis are introduced. They are calculated based on the error projection matrix in the back projection process. Secondly, a restraint item is adopted to constrain the back projection matrix. Thirdly, a modified iterative scheme is used to reconstruct the high resolution image. The effectiveness of the proposed approach on ringing artifacts suppression is demonstrated by experiments. The results indicate that the proposed method can achieve satisfactory performance. A new algorithm is put forward in this paper to suppress and reduce the ringing artifacts of the IBP super resolution algorithm.An iterative scheme is presented in this paper to get the more accurate image for SR reconstruction.In the algorithm, we take into self-adapting analysis based on the variation on projections and local variance for projection matrix between two iterations.In this paper, a restraint item is designed to restrain the back projection matrix and the iteration process, which based on the self-adapting analysis.

We propose an adaptive spatial postprocessing algorithm for using in block-based discrete cosine transform (BDCT) coded images. The proposed method is comprised of three procedural steps: a thresholding step, a model classification step, and a deblocking filtering step. First, we apply adaptive thresholding to extract the pixel vector containing the blocking artifacts. This threshold has a strong correlation with image quality and the standard deviation of the pixel vector. Next, we update block types using a simple rule, thus influencing deblocking performance. Our research proposes that the activity of the pixel vector can be used to categorize the pixel vector model. With the pixel vector model determined, we are then able to apply a suitable filter to each model with different local properties. For example, we are able to utilize a directional filter to reduce ringing artifacts in edge regions. With various images and bit-rate conditions, images deblocked by the proposed method exhibit both significant visual quality improvement and PSNR gain, with fairly low computational complexity.

We propose an edge based single image super-resolution algorithm.Our algorithm involves learning how ramp profiles transform across resolutions.Ramp profiles are defined as monotonic pixel profiles across edges.Our results demonstrate several advantages compared to other techniques. We propose the use of explicitly identified image structure to guide the solution of the single image super-resolution (SR) problem. We treat the image as a layout of homogeneous regions, surrounded by ramp edges of a larger contrast. Ramps are characterized by the property that any path through any ramp pixel, monotonically leading from one to the other side, has monotonically increasing (or decreasing) intensity values along it. Such a ramp profile thus captures the large contrast between the two homogeneous regions. In this paper, the SR problem is viewed primarily as one of super-resolving these ramps, since the relatively homogeneous interiors can be handled using simpler methods. Our approach involves learning how these ramps transform across resolutions, and applying the learnt transformations to the ramps of a test image. To obtain our final SR reconstruction, we use the transformed ramps as priors in a regularization framework, where the traditional backprojection constraint is used as the data term. As compared to conventional edge based SR methods, our approach provides three distinct advantages: (1) Conventional edge based SR methods are based on gradients, which use 2D filters with heuristically chosen parameters and these choices result in different gradient values. This sensitivity adversely affects learning gradient domain correspondences across different resolutions. We show that ramp profiles are more adaptive, stable and therefore reliable representations for learning edge transformations across resolutions. (2) Existing gradient based SR methods are often unable to sufficiently constrain the absolute brightness levels in the image. Our approach on the other hand, operates directly in the image intensity domain, enforcing sharpness as well as brightness consistency. (3) Unlike previous gradient based methods, we also explicitly incorporate dependency between closely spaced edges while learning ramp correspondences. This allows for better recovery of contrast across thin structures such as in high spatial frequency areas. We obtain results that are sharper and more faithful to the true image color, and show almost no ringing artifacts.

This paper presents a novel blind image deconvolution algorithm for motion deblurring from a single blurred image. We propose a unified framework for both blur kernel estimation and non-blind image deconvolution by using bilateral filtering (BF) and a new image deconvolution algorithm, called the Gradient Attenuation Richardson-Lucy (GARL) algorithm. In the blur kernel estimation stage, we show that an initial blur kernel, which is used for starting an alternating kernel refinement process, can be obtained from the blurred image with a quadratic regularization approach. In the non-blind image deconvolution stage, we exploit the image gradients and develop the GARL algorithm to alleviate the notorious ringing problem in the Richardson-Lucy-based image restoration approach. Furthermore, the loss of image details due to the suppression of the ringing artifacts around the regions with strong edges is recovered with an incremental detail recovery procedure. The proposed framework is simple yet effective compared to previous statistical approaches. Experimental results on various real data sets are given to demonstrate superior performance of the proposed algorithm over the previous methods. A novel blind image deconvolution algorithm for motion deblurring from a single blurred image is presented.A novel GARL algorithm is proposed to alleviate the ringing problem in the classical Richardson-Lucy image deconvolution method.A unified framework for blur kernel estimation and image deconvolution based on bilateral filtering and the GARL algorithm is presented.

Multi-frame image super-resolution (SR) aims to utilize information from a set of low-resolution (LR) images to compose a high-resolution (HR) one. In this paper, a novel multi-frame image super-resolution algorithm is proposed based on regional pixel information and ringing artifacts suppression. Firstly, a new regularization term which adopts Regional Adaptive Weight Coefficients (RAWC) is produced to keep edges and flat regions. After detailed analysis, an iterative process is given for image reconstruction. Then an adaptive term according to the local variance of iterative correction image is designed to evaluate the ringing artifacts. Finally, the original iteration is updated by adding the restraint term for better visual effects and lower noise of reconstructive HR image. Thorough experimental results show the proposed algorithm is effective for SR reconstruction and ringing artifacts suppression.

This paper presents a novel region-based framework for multifocus image fusion. The core idea is to segment the in-focus regions from the input images and merge them up to produce an all-in-focus image. To this end, we propose three intuitive constraints on the fusion process and model them into three energy terms, i.e., reconstruction error, out-of-focus energy and smoothness regularization. The three terms are then formulated into an optimization framework problem to solve a segmentation map. We also propose a greedy algorithm to minimize the objective function, which alternatively updates each pixel in the segmentation map using a coarse-to-fine strategy. The fused image is finally generated by combining the segmented in-focus regions in each source image via the segmentation map. Our approach can yield a seamless result with much fewer ringing artifacts. Comparative experiments based on various synthesized and real images demonstrate that our approach outperforms the state-of-the-art methods. (C) 2014 Elsevier B.V. All rights reserved.

In this paper we have proposed a single image motion deblurring algorithm that is based on a novel use of dual Fourier spectrum combined with bit plane slicing algorithm and Radon transform (RT) for accurate estimation of PSF parameters such as, blur length and blur angle. Even after very accurate PSF estimation, the deconvolution algorithms tend to introduce ringing artifacts at boundaries and near strong edges. To prevent this post deconvolution effect, a post processing method is also proposed in the framework of traditional Richardson–Lucy (RL) deconvolution algorithm. Experimental results evaluated on the basis of both qualitative and quantitative (PSNR, SSIM) metrics, verified on the dataset of both grayscale and color blurred images show that the proposed method outperforms the existing algorithms for removal of uniform blur. A comparison with state-of-the-art methods proves the usefulness of the proposed algorithm for deblurring real-life images/photographs.

Transform coding has been widely used in video coding standards, such as H.264 advanced video coding (H.264/AVC) and high efficiency video coding (HEVC). But the coded video sequences suffer from annoying coding artifacts, such as blocking and ringing artifacts. In this paper, we propose the quadtree-based non-local Kuan’s (QNLK) filter to suppress the quantization noise optimally and improve the objective and subjective quality of the reconstructed frame simultaneously. The proposed filter takes advantage of the non-local Kuan’s (NLK) filter to restore the quantized signal in transform domain. Restored coefficients are then projected onto designed quantization constraint sets (QCS). Quadtree-based signaling strategy is used at the end of QNLK for adaptive filtering on/off control. Experimental results of QNLK show that the proposed method achieves significant objective coding gain and visual quality improvement, compared with both H.264/AVC high profile and HEVC.

In this paper, we propose a modified non-blind Richardson–Lucy algorithm using adaptive reference maps as local constraint to reduce noise and ringing artifacts effectively. The deconvolution process can be divided into two stages. In the first deblurring stage, the reference map is estimated from the blurred image and an intermediate deblurred result is obtained. And then the adaptive reference map is updated according to both the blurred image and the deblurred result of the first stage to produce a more accurate edge description, which is very helpful to suppress the ringing around edges. Gaussian image prior is adopted as the regularization to improve the standard Richardson–Lucy algorithm. Experimental results show that the presented approach could suppress the negative ringing artifacts effectively as well as preserve the edge information, even if the blurred image contains rich textures.

We develop here an efficient approach using singular spectral analysis (SSA) for frequency filtering of seismic reflection data in t-x domain. The abrupt change in geophysical records creates ringing artifacts in the Fourier based filtering operations. We use here complete data adaptive basis functions in SSA filtering, which enables the self-similarity of the data in reconstruction of such sudden changes. We first tested the SSA based filtering algorithm on synthetic seismic data and then applied to real seismic reflection data from Singareni coalfields, Andhra Pradesh, India. The individual trace from each channel in the shot gathers is processed and compared with Fourier and multichannel SSA filtered output. Our analysis demonstrates that SSA filtering attenuated the low frequency ground role and high frequency noise embedded in the seismic record in a more efficient way than the other two methods. The coal formations and faults identified in the stack section of filtered data match quite well with the geological information available in the study region.

In multiview video plus depth (MVD) format, virtual views are generated from decoded texture videos with corresponding decoded depth images through depth image based rendering (DIBR). 3DV-ATM is a reference model for the H.264/AVC based multiview video coding (MVC) and aims at achieving high coding efficiency for 3D video in MVD format. Depth images are first downsampled then coded by 3DV-ATM. However, sharp object boundary characteristic of depth images does not well match with the transform coding based nature of H.264/AVC in 3DV-ATM. Depth boundaries are often blurred with ringing artifacts in the decoded depth images that result in noticeable artifacts in synthesized virtual views. This paper presents a low complexity adaptive depth truncation filter to recover the sharp object boundaries of the depth images using adaptive block repositioning and expansion for increasing the depth values refinement accuracy. This new approach is very efficient and can avoid false depth boundary refinement when block boundaries lie around the depth edge regions and ensure sufficient information within the processing block for depth layers classification. Experimental results demonstrate that the sharp depth edges can be recovered using the proposed filter and boundary artifacts in the synthesized views can be removed. The proposed method can provide improvement up to 3.25dB in the depth map enhancement and bitrate reduction of 3.06% in the synthesized views.

Although deep convolutional neural network has been proved to efficiently eliminate coding artifacts caused by the coarse quantization of traditional codec, it's difficult to train any neural network in front of the encoder for gradient's back-propagation. In this paper, we propose an end-to-end image compression framework based on convolutional neural network to resolve the problem of non-differentiability of the quantization function in the standard codec. First, the feature description neural network is used to get a valid description in the low-dimension space with respect to the ground-truth image so that the amount of image data is greatly reduced for storage or transmission. After image's valid description, standard image codec such as JPEG is leveraged to further compress image, which leads to image's great distortion and compression artifacts, especially blocking artifacts, detail missing, blurring, and ringing artifacts. Then, we use a post-processing neural network to remove these artifacts. Due to the challenge of directly learning a non-linear function for a standard codec based on convolutional neural network, we propose to learn a virtual codec neural network to approximate the projection from the valid description image to the post-processed compressed image, so that the gradient could be efficiently back-propagated from the post-processing neural network to the feature description neural network during training. Meanwhile, an advanced learning algorithm is proposed to train our deep neural networks for compression. Obviously, the priority of the proposed method is compatible with standard existing codecs and our learning strategy can be easily extended into these codecs based on convolutional neural network. Experimental results have demonstrated the advances of the proposed method as compared to several state-of-the-art approaches, especially at very low bit-rate., 11 pages, 7 figures

Since non-blind image deconvolution is inherently ill-posed, the results of unregularized methods are often contaminated by noise and ringing artifacts. To reach a stable solution, we adopt the natural image gradient prior to regularize the latent image and obtain an improved version of the Richardson–Lucy (RL) algorithm. We use both synthetic and real world blurred images to test the proposed method. Experimental results show that the negative artifacts are significantly suppressed and the restored images are of high quality.

In light-limited situations, camera motion blur is one of the prime causes for poor image quality. Recovering the blur kernel and latent image from the blurred observation is an inherently ill-posed problem. In this paper, we introduce a hand-held multispectral camera to capture a pair of blurred image and Near-InfraRed (NIR) flash image simultaneously and analyze the correlation between the pair of images. To utilize the high-frequency details of the scene captured by the NIR-flash image, we exploit the NIR gradient constraint as a new type of image regularization, and integrate it into a Maximum-A-Posteriori (MAP) problem to iteratively perform the kernel estimation and image restoration. We demonstrate our method on the synthetic and real images with both spatially invariant and spatially varying blur. The experiments strongly support the effectiveness of our method to provide both accurate kernel estimation and superior latent image with more details and fewer ringing artifacts.

This paper presents a deblurring method that effectively restores fine textures and details, such as a tree's leaves or regular patterns, and suppresses noises in flat regions using consecutively captured blurry and noisy images. To accomplish this, we used a method that combines noisy image updating with one iteration and fast deconvolution with spatially varying norms in a modified alternating minimization scheme. The captured noisy image is first denoised with a nonlocal means (NL-means) denoising method, and then fused with a deconvolved version of the captured blurred image on the frequency domain, to provide an initially restored image with less noise. Through a feedback loop, the captured noisy image is directly substituted with the initially restored image for one more NL-means denoising, which results in an upgraded noisy image with clearer outlines and less noise. Next, an alpha map that stores spatially varying norm values, which indicate local gradient priors in a maximum-a-posterior (MAP) estimation, is created based on texture likelihoods found by applying a texture detector to the initially restored image. The alpha map is used in a modified alternating minimization scheme with the pair of upgraded noisy images and a corresponding point spread function (PSF) to improve texture representation and suppress noises and ringing artifacts. Our results show that the proposed method effectively restores details and textures and alleviates noises in flat regions.

Single image deblurring is a highly ill-posed problem and requires to be regularized. Many common forms of image prior have a major drawback that is unable to make full use of local image information. In this paper, we propose a single image deblurring method using novel image prior constraints. We establish a probabilistic model by enforcing inspired image prior constraints and adopt an advanced iterative scheme that alternates between blur kernel estimation and non-blind image restoration. To suppress ringing artifacts caused by inevitable blur kernel estimated errors, our method employs total variation image restoration and presents an alternation half-quadratic algorithm to solve the non-convex cost function. Finally, experiments show that our method has good performance in suppressing ringing artifacts, and makes a good balance between alleviating staircase effects and preserving image details.

Two parameters of a point spread function (PSF) are the length and angle of the blur kernel. The deblurring performance is affected by the estimation accuracy of the two PSF parameters. In this paper, we propose a method of the PSF parameters estimation and the image reconstruction by using the periodicity of motion blurred images in the frequency domain and propose the reconstruction method using the proposed post-processing for reducing ringing artifacts. Experimental results show that the proposed method estimates the PSF parameters more accurately than the existing ones.

Fuzzy filtering has recently been applied and optimized for reducing distortion in compressed images and video. In this paper, we present a method combining the powerful anisotropic diffusion equations with fuzzy filtering for removing blocking and ringing artifacts. Due to the directional nature of these artifacts, we have applied directional anisotropic diffusion. In order to improve the performance of the algorithm, we select the threshold parameter for the diffusion coefficient adaptively. Two different methods based on this approach are presented: one designed for still images and the other for YUV video sequences. For the video sequences, different filters are applied to luminance (Y) and chrominance (U,V) components. The performance of the proposed method has been compared against several other methods by using different objective quality metrics and a subjective comparison study. Both objective and subjective results on JPEG compressed images, as well as MJPEG and H.264/AVC compressed video, indicate that the proposed algorithms employing directional and spatial fuzzy filters achieve better artifact reduction than other methods. In particular, robust improvements with H.264/AVC video have been gained with several different content types.

Conventional deblurring approaches such as the Richardson–Lucy (RL) algorithm will introduce strong noise and ringing artifacts, though the point spread function (PSF) is known. Since it is difficult to estimate an accurate PSF in real imaging system, the results of those algorithms will be worse. A spatial weight matrix (SWM) is adopted as local constraint, which is incorporated into image statistical prior to improve the RL approach. Experiments show that our approach can make a good balance between preserving image details and suppressing ringing artifacts and noise.

Non-blind image deconvolution is a process that obtains a sharp latent image from a blurred image when a point spread function (PSF) is known. However, ringing and noise amplification are inevitable artifacts in image deconvolution since perfect PSF estimation is impossible. The conventional regularization to reduce these artifacts cannot preserve image details in the deconvolved image when PSF estimation error is large, so strong regularization is needed. We propose a non-blind image deconvolution method which preserves image details, while suppressing ringing and noise artifacts by controlling regularization strength according to local characteristics of the image. In addition, the proposed method is performed fast with fast Fourier transforms so that it can be a practical solution to image deblurring problems. From experimental results, we have verified that the proposed method restored the sharp latent image with significantly reduced artifacts and it was performed fast compared to other non-blind image deconvolution methods.

As it is not possible to obtain an accurate point spread function (PSF) in remote sensing imaging, classic deconvolution methods such as Wiener filtering often introduce strong noise and ringing artifacts, which contaminate the restored images. In this paper, we modify the standard Richarson–Lucy (RL) algorithm with a piecewise local regularization term and combine it with residual deconvolution method. Experimental results show that it is effective in suppressing negative effects, and images with rich details and sharp edges are obtained.

Blocking artifacts exist in images and video sequences compressed to low bit rates using block-based discrete cosine transform (DCT) compression standards. In order to reduce blocking artifacts, two image postprocessing techniques, DNLK filter and OCDNLK filter, are presented in this paper. A more accurate DCT domain Kuan's filter based on Non-local parameter estimation was proposed from the linear minimum mean-square-error (MMSE) criterion. We analyze the required two assumptions for the filter theoretically. Then the DCT domain Kuan's filter for low frequency coefficients and Non-local mean filter for high frequency AC coefficients constitute the proposed Non-local Kuan's (NLK) filter. After that, we propose the Dual Non-local Kuan's (DNLK) filter by applying the proposed filter in dual layer. The DNLK filter is extended to form the Overcomplete Dual Non-local Kuan's (OCDNLK) filter by applying to the overcomplete DCT coefficients. Experimental results on coded images using test quantization tables and JPEG coded images show the effectiveness of the two methods.

Objective In general, low-field MRI scanners such as the 0.5- and 1-T ones produce images that are poor in quality. The motivation of this study was to lessen the noise and enhance the signal such that the image quality is improved. Here, we propose a new approach using stochastic resonance (SR)-based transform in Fourier space for the enhancement of magnetic resonance images of brain lesions, by utilizing an optimized level of Gaussian fluctuation that maximizes signal-to-noise ratio (SNR). Materials and Methods We acquired the T1-weighted MR image of the brain in DICOM format. We processed the original MR image using the proposed SR procedure. We then tested our approach on about 60 patients of different age groups with different lesions, such as arteriovenous malformation, benign lesion and malignant tumor, and illustrated the image enhancement by using just-noticeable difference visually as well as by utilizing the relative enhancement factor quantitatively. Results Our method can restore the original image from noisy image and optimally enhance the edges or boundaries of the tissues, clarify indistinct structural brain lesions without producing ringing artifacts, as well as delineate the edematous area, active tumor zone, lesion heterogeneity or morphology, and vascular abnormality. The proposed technique improves the enhancement factor better than the conventional techniques like the Wiener- and wavelet-based procedures. Conclusions The proposed method can readily enhance the image fusing a unique constructive interaction of noise and signal, and enables improved diagnosis over conventional methods. The approach well illustrates the novel potential of using a small amount of Gaussian noise to improve the image quality.

Analog filtering and digital signal processing algorithms in the preprocessing modules of an electrocardiographic device play a pivotal role in providing high-quality electrocardiogram (ECG) signals for analysis, interpretation, and presentation (display, printout, and storage). In this article, issues relating to inaccuracy of ECG preprocessing filters are investigated in the context of facilitating efficient ECG interpretation and diagnosis. The discussion covers 4 specific ECG preprocessing applications: anti-aliasing and upper-frequency cutoff, baseline wander suppression and lower-frequency cutoff, line frequency rejection, and muscle artifact reduction. Issues discussed include linear phase, aliasing, distortion, ringing, and attenuation of desired ECG signals. Due to the overlapping power spectrum of signal and noise in acquired ECG data, frequency selective filters must seek a delicate balance between noise removal and deformation of the desired signal. Most importantly, the filtering output should not adversely impact subsequent diagnosis and interpretation. Based on these discussions, several suggestions are made to improve and update existing ECG data preprocessing standards and guidelines.

Ringing is one of the most common disturbing artifacts in image deconvolution. With a totally known kernel, the standard Richardson–Lucy (RL) algorithm succeeds in many motion deblurring processes, but the resulting images still contain visible ringing. When the estimated kernel is different from the real one, the result of the standard RL iterative algorithm will be worse. To suppress the ringing artifacts caused by failures in the blur kernel estimation, this paper improves the RL algorithm based on the local prior. Firstly, the standard deviation of pixels in the local window is computed to find the smooth region and the image gradient in the region is constrained to make its distribution consistent with the deblurring image gradient. Secondly, in order to suppress the ringing near the edge of a rigid body in the image, a new mask was obtained by computing the sharp edge of the image produced using the first step. If the kernel is large-scale, where the foreground is rigid and the background is smoothing, this step could produce a significant inhibitory effect on ringing artifacts. Thirdly, the boundary constraint is strengthened if the boundary is relatively smooth. As a result of the steps above, high-quality deblurred images can be obtained even when the estimated kernels are not perfectly accurate. On the basis of blurred images and the related kernel information taken by the additional hardware, our approach proved to be effective.

A simple and easy to implement technique for improving block truncation coding (BTC) is proposed. The method is based on replacement of bit block obtained using the conventional BTC method with the fuzzy logical bit block (LBB) such that the sample mean and standard deviation in each image block are preserved. This fuzzy LBB is obtained from the fuzzy edge image by using the Yager involutive fuzzy complement edge operator (YIFCEO). The input image is encoded with the block mean and standard deviation and the fuzzy LBB. Experimental results show an improvement of visual quality of reconstructed images and peak signal-to-noise ratio (PSNR) when compared to the conventional BTC. The raggedness and jagged appearance and the ringing artifacts at sharp edges are greatly reduced in the reconstructed images. With the use of YIFCEO, the proposed method is shown to be more flexible to determine the visual quality of the reconstructed images.

A recent study by Power and colleagues shows that BOLD artifacts induced by head movement can substantially alter patterns of resting-state functional connectivity and proposes a novel procedure for reducing these artifacts by deleting (or "scrubbing") movement-contaminated volumes. The authors acknowledge that this work is descriptive and not prescriptive, and note that future studies may refine the proposed scrubbing method. Nevertheless, it is worth pointing out that this method can be improved substantially by a single transposition in the order of operations. Temporal filtering is known to introduce ringing artifacts that emanate from sharp transitions in signal intensity. The method proposed in the target article applies temporal filtering before deleting contaminated volumes-in effect, spreading movement-related artifacts backwards and forwards in time, but deleting only the originally contaminated data. Using simulated data, we show that deleting and replacing contaminated volumes before temporal filtering removes a greater proportion of artifactual signal while retaining a greater proportion of the original data.

In this paper, we present a method to reduce blocking and ringing artifacts in low bit-rate block-based video coding. For each block, its DC value and DC values of the surrounding eight neighbor blocks are exploited to predict low frequency AC coefficients, which allow to infer spatial characteristics of a block before quantization stage in the encoding system. These predicted AC coefficients are used to classify each block into either of two categories, low-activity or high-activity block. In the following post-processing stage, two kinds of low pass filters are adaptively applied according to the classified result on each block. It allows for strong low pass filtering in low-activity regions where the blocking artifacts are most noticeable, whereas it allows for weak low pass filtering in high-activity regions to reduce ringing noise as well as blocking artifacts without introducing undesired blur. In the former case, the blocking artifacts are reduced by one-dimensional (1-D) horizontal and vertical low pass filters. In the latter case, both deblocking and deringing are considered by using either 3-tap or 2-tap filter, which make the architecture simple. TMN8 decoder for H.263+ is used to test the proposed method. The experimental results are evaluated in both subjective and objective manner, and show that the proposed algorithm is efficient and effective in reducing ringing artifacts as well as blocking artifacts in the low bit-rate block-based video coding.

This paper considers the well known problem of ringing distortion in subband coded images. Exploiting the fact that ringing artifacts are masked by edges and textures we propose a new method designed to remove all ringing distortion within the smooth image areas. The original image is used for classification purposes and the subband coded image is postprocessed using blockwise paraboloid fitting algorithms in the smooth image areas. Experimental results comparing subband coded images with and without application of the proposed method are presented. The results indicate that significant visual improvements can be made at the expense of a very small increase in bit-rat.