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The time-of-flight (TOF) information makes Orlov’s condition not necessary for positron emission tomography (PET) imaging. One-view imaging has never been attempted before. This article investigates whether one-view imaging is possible. We claim that it is possible to obtain an image with measurements from only one-view, provided the TOF time resolution is good enough and the photon counts are high enough. In fact, the image value at a point can be reconstructed by measurements along one line of response (LOR) that passes through the point of interest. The region of interest (ROI) can be reconstructed with ray-by-ray deblurring methods, which make TOF PET a local tomography system. One-view imaging is a severely ill-posed problem if the TOF resolution is not good enough. One indicator of an ill-posed problem is the ringing artifacts in the reconstruction. We show that ringing artifacts can be eliminated by using the nonnegativity constraint for the dot-like objects with a zero background.

PURPOSE: To investigate and remove Gibbs-ringing artifacts caused by partial Fourier (PF) acquisition and zero filling interpolation in MRI data. THEORY AND METHODS: Gibbs ringing of fully sampled data, leading to oscillations around tissue boundaries, is caused by the symmetric truncation of k-space. Such ringing can be removed by conventional methods, with the local subvoxel shifts method being the state-of-the-art. However, the asymmetric truncation of k-space in routinely used PF acquisitions leads to additional ringings of wider intervals in the PF sampling dimension that cannot be corrected solely based on magnitude images reconstructed via zero filling. Here, we develop a pipeline for the Removal of PF-induced Gibbs ringing (RPG) to remove ringing patterns of different periods by applying the conventional method twice. The proposed pipeline is validated on numerical phantoms, demonstrated on in vivo diffusion MRI measurements, and compared with the conventional method and neural network-based approach. RESULTS: For PF = 7/8 and 6/8, Gibbs-ringings and subsequent bias in diffusion metrics induced by PF acquisition and zero filling are robustly removed by using the proposed RPG pipeline. For PF = 5/8, however, ringing removal via RPG leads to excessive image blurring due to the interplay of image phase and convolution kernel. CONCLUSIONS: RPG corrects Gibbs-ringing artifacts in magnitude images of PF acquired data and reduces the bias in quantitative MR metrics. Considering the benefit of PF acquisition and the feasibility of ringing removal, we suggest applying PF = 6/8 when PF acquisition is necessary.

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

Medical image compression plays a vital role in diagnosis of diseases which allowing manipulation, efficient, transmission and storage of color, binary and grayscale image. Before transmission and storage, a medical image may be required to be compressed. The objective of the study is to develop an efficient and effective technique for digital medical images which alleviates the blocking artifacts from grayscale image while retaining all relevant structures. In this paper, we demonstrate a highly engineered postprocessing filtering approach has been designed to remove blocking effects from medical images at low bit rate. The proposed technique is comprised of three strategies i.e. 1) a threshold valve scheme which is used to capture the pixel vectors containing blocking artifacts. 2) Blocking artifacts measurement techniques. The blocking artifacts are measured by three frequency related modes (low, Moderate and high frequency model). 3) A directional filter which is used to remove over-smoothing and ringing artifacts near edges of block boundary. The algorithm is tested on digital medical grayscale images from different modalities. The experimental results illustrate that the proposed technique is more efficient on the basis of PSNR-B, MSSIM, and MOS indices than the state-of-the-art methods. The proposed algorithm can be seamlessly applied in area of medical image compression which high transmission efficiency and acceptable image quality can be guaranteed.

Multi-spectralimages suffers from out-of-focusblur due to well focused camera at the reference imaging channel. A framework for out-of-focus images de-blurring using texture extraction and modified Fourier transform is proposed. The texture is extracted from the blurred image using region covariance. Fourier transform is modified by modification of guided image (as prior) using L0 gradient projection followed by detail amplification which is used as an input for Fourier transform. For further enhancement, the multi-spectral de-blurred images are smooth, and ringing artifacts are minimized by combining with the textures extracted to ensure maximum edge preservation. Comparison of proposed and existing schemes on different multi-spectral images explains the advantage of the proposed scheme for de-blurring in terms of edge preservation, noise and artifacts removal.

In this paper, we propose a high quality image deblurring method that uses discrete cosine transform (DCT) and requires less computational complexity. We train our model on a new dataset which is customized to include images with large motion blurs. Recently, Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) based algorithms have been proposed for image deblurring. Moreover, multi-scale and multi-patch architectures of CNN restore blurred images clearly and suppress more ringing or blocking artifacts, but they take a longer time to process. To improve the quality of deblured images and reduce the computational time, we propose a method called “DeblurDCTGAN” that preserves texture and suppresses ringing artifacts in the restored image without multi-scale or multi-patch architecture using DCT based loss. This loss compares the restored image and the ground truth image in the frequency domain. With this loss, DeblurDCTGAN can reduce block noise and ringing artifacts while maintaining deblurring performance. Our experimental results show that DeblurDCTGAN gets the highest performances in terms of PSNR, SSIM, and running time compared with conventional methods. In terms of real image datasets, DeblurDCTGAN shows a better performance by using a customized training dataset made from GoPro, DVD, NFS and HIDE training datasets. Experimented code with pre-trained weights, datasets and results are available at https://github.com/Hiroki-Tomosada/DCTGAN-master

PURPOSE Metabolic magnetic resonance imaging (MRI) using hyperpolarized [1-13 C]-pyruvate offers unprecedented new insight into disease and response to therapy. 13 C-enriched reference standards are required to enable fast and accurate calibration for 13 C studies, but care must be taken to ensure that the reference is compatible with both 13 C and 1 H acquisitions. The goal of this study was to optimize the composition of a 13 C-urea reference for a dual-tuned 13 C/1 H endorectal coil and minimize imaging artifacts in metabolic and multiparametric MRI studies involving hyperpolarized [1-13 C]-pyruvate. METHODS Due to a high amount of Gd doping for the purpose of reducing the spin-lattice relaxation time (T1 ) of urea, the 1 H signal produced by a reference of 13 C-urea in normal water was rapidly relaxed, resulting in severe artifacts in heavily T1 -weighted images. Hyperintense ringing artifacts in 1 H images were mitigated by reducing the 1 H concentration in a 13 C-urea reference via deuteration and lyophilization. Several references were fabricated and their SNR was compared using 1 H and 13 C imaging sequences on a 3T MRI scanner. Finally, 1 H prostate phantom imaging was conducted to compare image quality and 1 H signal intensity of normal and deuterated urea references. RESULTS The deuterated 13 C-urea reference provides strong 13 C signal for calibration and an attenuated 1 H signal that does not interfere with heavily T1 -weighted scans. Deuteration and lyophilization were fundamental to the reduction in 1 H signal and hyperintense ringing artifacts. There was a 25-fold reduction in signal intensity when comparing the nondeuterated reference to the deuterated reference, while the 13 C signal was unaffected. CONCLUSION A deuterated reference reduced hyperintense ringing artifacts in 1 H images by reducing the 1 H signal produced from the 13 C-urea in the reference. The deuterated reference can be used to improve anatomical image quality in future clinical 1 H and hyperpolarized [1-13 C]-pyruvate MRI prostate imaging studies.

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

Lossy compression brings artifacts into the compressed image and degrades the visual quality. In recent years, many compression artifacts removal methods based on convolutional neural network (CNN) have been developed with great success. However, these methods usually train a model based on one specific value or a small range of quality factors. Obviously, if the test image's quality factor does not match to the assumed value range, then degraded performance will be resulted. With this motivation and further consideration of practical usage, a highly robust compression artifacts removal network is proposed in this paper. Our proposed network is a single model approach that can be trained for handling a wide range of quality factors while consistently delivering superior or comparable image artifacts removal performance. To demonstrate, we focus on the JPEG compression with quality factors, ranging from 1 to 60. Note that a turnkey success of our proposed network lies in the novel utilization of the quantization tables as part of the training data. Furthermore, it has two branches in parallel---i.e., the restoration branch and the global branch. The former effectively removes the local artifacts, such as ringing artifacts removal. On the other hand, the latter extracts the global features of the entire image that provides highly instrumental image quality improvement, especially effective on dealing with the global artifacts, such as blocking, color shifting. Extensive experimental results performed on color and grayscale images have clearly demonstrated the effectiveness and efficacy of our proposed single-model approach on the removal of compression artifacts from the decoded image., Comment: Accepted for publication in the IEEE Transactions on Image Processing

Blind image deblurring, composed of estimating blur kernel and non-blind deconvolution, is an extremely ill-posed problem. However, previous deblurring methods still cannot solve delta kernel or noise problem well and avoid ringing artifacts in restored image, because the reliable kernel estimation and image restoration could not be given from information-deficiency input without using natural image priors. In this work, we find that the blur process changes the distribution of the image gradient, and thus attempt to use the priori knowledge for guiding blind deblurring. For the sake of convenience of modeling, we come up with a simplified approximate formulation of the image gradient distribution prior, thus the restoration model using it can be solved by the method of iteratively reweighted least squares (IRLS). We also concentrate on how to optimize the models and develop an algorithm based on gradient prior and image structure: first, we computed image structure based on the total variation model and adaptive weight strategy and then estimated the strong edges from it. Those strong edges (structure) that have a possible adverse effect on blur kernel estimation can be removed. Next, we followed an alternate iterative framework to obtain high-quality blur kernel estimation by estimating blur kernel from strong structure and then restoring a latent image guided by the formulated clear image priors. Finally, we proposed a non-blind deconvolution method based on fitted multi-order gradient priors as regularization to restore the latent image. In addition, we analyze the effectiveness of the knowledge-driven prior in image deblurring and demonstrate that it can favor clear images over blurred ones and restrain ringing artifacts effectively. Extensive experiments verify the superiority of the proposed method over state-of-the-art algorithms compared, both qualitatively and quantitatively.

By merging high-resolution panchromatic (PAN) images with low-resolution multispectral (MS) images, high-resolution MS images with complementary information can be obtained, i.e., pansharpening. Multiresolution analysis (MRA) methods have attracted widespread attention in the pansharpening field. The spatial detail information injected into MS images is extracted from PAN images by MRA tools. Since such methods often suffer from spatial distortion and ringing artifacts, a restoration algorithm based on blind deblurring and iterative back-projection (IBP) is proposed in this paper. First, a blind deblurring method based on the Tikhonov regular constraint model is used to estimate the blurring filter. Second, spatial details extracted from PAN images are modulated into MS images using a high-pass modulation (HPM) framework, and then fusion images are spatially enhanced based on the modulation results and blurring filter. Finally, the IBP technique is used to project the reconstruction error back to iteratively update and optimize the desired high-resolution images. Experiments are performed on data sets acquired by different satellites at full and reduced resolution, and eight state-of-the-art MRA-based pansharpening methods are used for validation. Compared to the enhanced back-projection (EBP) algorithm, the proposed restoration method is better in improving spectral and spatial quality of MRA-based pansharpening. The results indicate the effectiveness and superiority of the proposed method.

This paper presents a simultaneous autoregressive (SAR) analysis method to describe the unknown signal-to-noise ratio (SNR) and the texture feature of low-quality real video frames when the ground-truth images are not available. The real video images degraded by the factors, such as electronic noise, oversaturated pixels, motion blur, and compression artifacts, often result in poor motion registration estimation, which makes the performance of the existing video super-resolution (VSR) algorithms lower than expected. It is hard to estimate the SNR of the low-quality real frames without any prior knowledge. To solve this problem, we made a connection between SAR hyperparameters and the SNR of real images. The relationship expression of them was given in this paper. Using the proposed method, well-registered low-quality real video frames can be selected to decrease the root mean squared error (RMSE) of motion estimation of video frames for VSR reconstruction improvement. The anomalous low-quality frame images whose SAR hyperparameters values are inconsistent with others will be considered for removal. Synthetic experiments were designed to illustrate how the SAR hyperparameters values vary with the variation of synthetic parameters. In order to better illustrate the effectiveness of the proposed method, real low-quality videos captured under different conditions were tested under VSR reconstruction experiments. The VSR reconstruction results show that the results obtained using SAR prior analysis have sharper edges and fewer ringing artifacts than the original results. It indicates that the proposed method is helpful to obtain better results of motion registration estimation for low-quality real video images.

This paper develops a detail image signal enhancement that makes images perceived as clearer and more resolved and so is more effective for higher resolution displays. We observe that the local variant signal enhancement makes images more vivid, and the more revealed granular signals harmonically embedded on the local variant signals make images more resolved. Based on this observation, we develop a method that not only emphasizes the local variant signals by scaling up the frequency energy in accordance with human visual perception, but also strengths up the granular signals by embedding the alpha-rooting enhanced frequency components. The proposed energy scaling method emphasizes the detail signals in texture images and rarely boosts noisy signals in plain images. In addition, to avoid the local ringing artifact, the proposed method adjusts the enhancement direction to be parallel to the underlying image signal direction. It was verified through the subjective and objective quality evaluations that the developed method makes images perceived as clearer and highly resolved.

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.

Motion blur exists in many computer vision tasks, including faces, texts, and low-illumination images etc. It has been proved that Dark Channel Prior (DCP) and Bright Channel Prior (BCP) can both help the image deblurring by enhancing the dark or bright channel pixels. However, the pixels between the dark channel pixels and the bright channel pixels are not taken into consideration, which limits the deblurring performance. A novel image channel is proposed in combination with dark channel and bright channel in this paper to consider the effects of the all types of pixels, namely, Michelson channel pixels. Secondly, as the image channels are built based on the series of image patches with different blur kernels, a new method is developed to estimate the blur kernel and can measure the similarity between neighbored kernels. Meanwhile, to perform accurate kernel estimation, the L0 regularization is applied into the algorithm framework. In the process of image deblurring, by enhancing the Michelson channels and retaining the other channels of the image, we can capture sharper image detail and eliminate the ringing artifacts of the recovered images. Massive experimental results demonstrate that the proposed method is more robust and outperforms the existing art-of-the-state of unsupervised image deblurring methods on both synthesized and natural images.

Converting a single low dynamic range (LDR) image into a high dynamic range (HDR) image, which is the so-called inverse tone mapping (ITM), is a challenging ill-posed problem since a lot of information is lost during compression and storage. Traditional ITM techniques mainly focus on high-quality LDR images without compression artifacts. However, in practice, LDR images are usually stored as a lossy compression format for the convenience of transmission, which will cause artifacts, i.e., blocking and ringing. Hence, these ITM methods suffer from severe performance drop for some real-world applications. In this paper, we propose a novel decomposition-based network to reconstruct HDR images from compressed LDR images; in other words, the proposed network can simultaneously remove blocking/ringing artifacts and recover high-quality HDR information. Considering compression artifacts mainly embedded in high frequency part, we thus decompose the input image into the high-frequency component (also known as detail layer) and low-frequency component (also known as a base layer). The detail layer mainly contains the information of texture, noise, and artifacts, while the base layer contains the information of structure and large object. Based on this, we design two subnetworks, i.e., detail layer recovery subnetwork and base layer recovery subnetwork, to restore the two parts separately. The detail layer recovery subnetwork is responsible for the artifacts removal with texture preserving, while the base layer recovery subnetwork is designed for tone expansion and overexposed/underexposed region restoration. Finally, in order to further reconstruct the serious overexposed regions, we adapt a merge subnetwork to fuse the result from the previous two subnetworks. The experimental results on compressed images demonstrate that the proposed method significantly outperforms other state-of-the-art methods.

Image priors have been successfully introduced to solve ill-posed problems, such as image denoising. In this paper, we propose a new denoising model for magnetic resonance images (MRIs) which employs the image low-rank and sparse gradient priors. First, we use a Gaussian mixture model (GMM) to guide the clustering of non-local self-similar patches by learning the structure of external noise-free MRI patches to help retain the low rank of the noisy MRI patch matrix. Second, we fit the heavy-tailed gradient of MRI with a hyper-Laplacian distribution to reduce ringing artifacts. Third, we adopt an alternating iterative algorithm. The experimental results show that our proposed algorithm outperforms many classical MRI denoising methods, such as unbiased nonlocal means (UNLM) filtering and block-matching and 3D (BM3D) filtering in both visual and numerical results.

MRI images are visually inspected by domain experts for the analysis and quantification of the tumorous tissues. Due to the large volumetric data, manual reporting on the images is subjective, cumbersome, and error prone. To address these problems, automatic image analysis tools are employed for tumor segmentation and other subsequent statistical analysis. However, prior to the tumor analysis and quantification, an important challenge lies in the pre-processing. In the present study, permutations of different pre-processing methods are comprehensively investigated. In particular, the study focused on Gibbs ringing artifact removal, bias field correction, intensity normalization, and adaptive histogram equalization (AHE). The pre-processed MRI data is then passed onto 3D U-Net for automatic segmentation of brain tumors. The segmentation results demonstrated the best performance with the combination of two techniques, i.e., Gibbs ringing artifact removal and bias-field correction. The proposed technique achieved mean dice score metrics of 0.91, 0.86, and 0.70 for the whole tumor, tumor core, and enhancing tumor, respectively. The testing mean dice scores achieved by the system are 0.90, 0.83, and 0.71 for the whole tumor, core tumor, and enhancing tumor, respectively. The novelty of this work concerns a robust pre-processing sequence for improving the segmentation accuracy of MR images. The proposed method overcame the testing dice scores of the state-of-the-art methods. The results are benchmarked with the existing techniques used in the Brain Tumor Segmentation Challenge (BraTS) 2018 challenge. 2021 by the authors. Licensee MDPI, Basel, Switzerland. Funding: The authors also thank the Higher Education Commission (HEC), Pakistan, for funding this research under the Artificial Intelligence in Healthcare, IIPL, National Center of Artificial Intelligence (NCAI) and partial funding by HEC-SRGP research grant. Scopus

Limited-angle data, such as data obtained from a dual-panel breast-PET scanner, result in substantial image blur in directions coinciding with the missing cone of the image spectrum. On systems with time-of-flight (TOF) capabilities, this blur is reduced as given by the TOF uncertainty, with the image spectrum being correspondingly expanded into the missing spectral cone. Modeling of the TOF uncertainty in the reconstruction is expected to deconvolve this residual TOF blurring. We have however observed that, as a tradeoff, this TOF de-blurring process also introduces ringing artifacts at the edges, analogous to the edge effects observed with line-of-response (LOR) resolution modeling, which attempts to deconvolve the blur due to detector resolution effects. However, in the former case, the ringing artifacts are much wider due to the spatial extent of the TOF uncertainty as compared to the width of the typical LOR resolution blur. We illustrate and investigate the effects of using matched, as well as under-modeled and over-modeled, TOF kernels on edge artifacts in reconstruction from limited-angle data, and compare them with TOF reconstructions of complete data. Although for the conventional data with full angular coverage the reconstruction is fairly insensitive to the exact size of the TOF kernel and TOF modeling does not produce ringing artifacts, it is not the case for the limited-angle data. We show that it is important to use some form of regularization of the TOF uncertainty deconvolution process within the reconstruction of the limited-angle data, such as decreasing the TOF kernel size.

Automatic speech recognition (ASR) systems have been widely deployed in modern smart devices to provide convenient and diverse voice-controlled services. Since ASR systems are vulnerable to audio replay attacks that can spoof and mislead ASR systems, a number of defense systems have been proposed to identify replayed audio signals based on the speakers' unique acoustic features in the frequency domain. In this paper, we uncover a new type of replay attack called modulated replay attack, which can bypass the existing frequency domain based defense systems. The basic idea is to compensate for the frequency distortion of a given electronic speaker using an inverse filter that is customized to the speaker's transform characteristics. Our experiments on real smart devices confirm the modulated replay attacks can successfully escape the existing detection mechanisms that rely on identifying suspicious features in the frequency domain. To defeat modulated replay attacks, we design and implement a countermeasure named DualGuard. We discover and formally prove that no matter how the replay audio signals could be modulated, the replay attacks will either leave ringing artifacts in the time domain or cause spectrum distortion in the frequency domain. Therefore, by jointly checking suspicious features in both frequency and time domains, DualGuard can successfully detect various replay attacks including the modulated replay attacks. We implement a prototype of DualGuard on a popular voice interactive platform, ReSpeaker Core v2. The experimental results show DualGuard can achieve 98% accuracy on detecting modulated replay attacks., 17 pages, 24 figures, In Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security (CCS' 20)

Pansharpening (PS) is a process used to generate high-resolution multispectral (MS) images from high-spatial-resolution panchromatic (PAN) and high-spectral-resolution multispectral images. In this paper, we propose a method for pansharpening by focusing on a compressed sensing (CS) technique. The spectral reproducibility of the CS technique is high due to its image reproducibility, but the reproduced image is blurry. Although methods of complementing this incomplete reproduction have been proposed, it is known that the existing method may cause ringing artifacts. On the other hand, component substitution is another technique used for pansharpening. It is expected that the spatial resolution of the images generated by this technique will be as high as that of the high-resolution PAN image, because the technique uses the corrected intensity calculated from the PAN image. Based on these facts, the proposed method fuses the intensity obtained by the component substitution method and the intensity obtained by the CS technique to move the spatial resolution of the reproduced image close to that of the PAN image while reducing the spectral distortion. Experimental results showed that the proposed method can reduce spectral distortion and maintain spatial resolution better than the existing methods.

Image deconvolution consists in restoring a blurred and noisy image knowing its point spread function (PSF). This inverse problem is ill-posed and needs prior information to obtain a satisfactory solution. Bayesian inference approach with appropriate prior on the image, in particular with a Gaussian prior, has been used successfully. Supervised Bayesian approach with maximum a posteriori (MAP) estimation, a method that has been considered recently, is unstable and suffers from serious ringing artifacts in many applications. To overcome these drawbacks, we propose a regularized version where we minimize an energy functional combined by the mean square error with H1 regularization term, and we consider the generalized cross validation (GCV) method, a widely used and very successful predictive approach, for choosing the smoothing parameter. Theoretically, we study the convergence behavior of the method and we give numerical tests to show its effectiveness.

Singular value decomposition-based clutter filters can robustly reject tissue clutter, allowing for detection of slow blood flow in imaging microvasculature. However, to identify microvessels, high ultrasound frequency must be used to increase the spatial resolution at the expense of shorter depth of penetration. Deconvolution using Tikhonov regularization is an imaging processing method widely used to improve spatial resolution. The ringing artifact of Tikhonov regularization, though, can produce image artifacts such as non-existent microvessels, which degrade image quality. Therefore, a deconvolution method using total variation is proposed in this study to improve spatial resolution and mitigate the ringing artifact. Performance of the proposed method was evaluated using chicken embryo brain, ex ovo chicken embryo chorioallantoic membrane and tumor data. Results revealed that the reconstructed power Doppler (PD) images are substantially improved in spatial resolution compared with original PD images: the full width half-maximum (FWHM) of the cross-sectional profile of a microvessel was improved from 132 to 83 μm. Two neighboring microvessels that were 154 μm apart were better separated using the proposed method than conventional PD imaging. Additionally, 223 FWHMs measured from the cross-sectional profiles of 223 vessels were used to determine the improvement in FWHM with the proposed method statistically. The mean ± standard deviation of the FWHM without and with the proposed method was 233.19 ± 85.08 and 172.31 ± 75.11 μm, respectively; the maximum FWHM without and with the proposed method was 693.01 and 668.69 μm; and the minimum FWHM without and with the proposed method was 73.92 and 45.74 μm. There were statistically significant differences between FWHMs with and without the proposed method according to the rank-sum test, p < 0.0001. The contrast-to-noise ratio improved from 1.06 to 4.03 dB with use of the proposed method. We also compared the proposed method with Tikhonov regularization using ex ovo chicken embryo chorioallantoic membrane data. We found that the proposed method outperformed Tikhonov regularization as false microvessels appeared using the Tikhonov regularization but not with the proposed method. These results indicate that the proposed method is capable of providing more robust PD images with higher spatial resolution and higher contrast-to-noise ratio.

We present a method to position point sources at arbitrary locations on finite-difference (FD) grids. We show that implementing point sources on single nodes can cause considerable errors when modelling with the FD method. In contrast, we propose to create a spatially distributed source (over multiple nodes) that nonetheless creates the desired point-source response. The spatial point source is formulated in the wavenumber domain and is based on the FD coefficients used for the wave propagation. Using this ‘FD-consistent source’ on 1-D and 2-D examples, we show that we can obtain superior fits to analytical solutions compared to single-node or sinc-function source implementations, and we show that sources can be offset to arbitrary locations from ‘on’ the grid to ‘off’ the grid, while resulting in solutions that are identical to within machine precision, Geophysical Journal International, 223 (2), ISSN:0956-540X, ISSN:1365-246X

Image resampling is a widely used tool in image processing. The upsampling increases the number of pixels and introduces new information to the image which can have undesired effects, like ringing artifacts and oscillations, aliasing “jagged” lines effect, or introduces too much numerical diffusion. Histopolation upsampling methods produce much sharper images but are more prone to aliasing and ringing effect and oscillations which appear as spurious signals near sharp transitions in color intensity. In this paper, we propose an efficient and fast quasi-histopolation algorithm based on the Canonical Complete Chebyshev–Schoenberg operator approximations, applied dimension by dimension. These approximations, because of their shape preserving properties, avoid oscillations. Presented methods have several tunable parameters that control tension and shape properties of the approximation which are used to reduce the aliasing effect while keeping the image visually sharp. Numerical tests on real and artificial images demonstrate the effectiveness and show the computational efficiency of the proposed algorithm.

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.

A function that is well suited to describe a square wave as well as a sequence of delta functions is presented. The squdel function has a closed rational form instead of a series approximation. Bandwidth limitations are readily incorporated in this function without producing undesirable ringing artifacts. The squdel function is infinitely differentiable and analytic for a squareness parameter as close as required to the square function provided that the limit is not taken. Even its Fourier series decomposition does not exhibit overshooting when truncated. Two-dimensional soft pixel structures are shown to be economically modeled with this function.

The autoregressive (AR) model is widely used in image interpolations. Traditional AR models consider utilizing the dependence between pixels to model the image signal. However, they ignore the valuable patch-level information for image modeling. In this paper, we propose to integrate both the pixel-level and patch-level information to depict the relationship between high-resolution and low-resolution pixels and obtain better image interpolation results. In particular, we propose an isophote-constrained AR (ICAR) model to perform AR-flavored interpolation within an identified joint stable region and further develop an AR interpolation with an adaptive window extension. Considering the smoothness along the isophote curve, the ICAR model searches only several successive similar patches along the isophote curve over a large region to construct an adaptive window. These overlapped patches, representing the patch-level structure similarity, are used to construct a joint AR model. To better characterize the piecewise stationarity and determine whether a pixel is suitable for AR estimation, we further propose pixel-level and patch-level similarity metrics and embed them into the ICAR model, introducing a weighted ICAR model. Comprehensive experiments demonstrate that our method can effectively reconstruct the edge structures and suppress jaggy or ringing artifacts. In the objective quality evaluation, our method achieves the best results in terms of both peak signal-to-noise ratio and structural similarity for both simple size doubling (two times) and for arbitrary scale enlargements.

In this paper, we present a simple yet effective image deblurring method to produce ringing-free deblurred images. Our work is inspired by the observation that large-scale deblurring ringing artifacts are measurable through a multi-resolution pyramid of low-pass filtering of the blurred-deblurred image pair. We propose to model such a quantification as a convex cost function and minimize it directly in the deblurring process in order to reduce ringing regardless of its cause. An efficient primal-dual algorithm is proposed as a solution to this optimization problem. Since the regularization is more biased toward ringing patterns, the details of the reconstructed image are prevented from over-smoothing. An inevitable source of ringing is sensor saturation which can be detected costlessly contrary to most other sources of ringing. However, dealing with the saturation effect in deblurring introduces a non-linear operator in optimization problem. In this paper, we also introduce a linear approximation as a solution to handling saturation in the proposed deblurring method. As a result of these steps, we significantly enhance the quality of the deblurred images. Experimental results and quantitative evaluations demonstrate that the proposed method performs favorably against 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.

This study aims to introduce a new compressed sensing averaging (CSA) technique for the reduction of blurring and/or ringing artifacts, depending on the k-space sampling ratio. A full k-space dataset and three randomly undersampled datasets were obtained for CSA images in a brain phantom and a healthy subject. An additional simulation was performed to assess the effect of the undersampling ratio on the images and the signal-to-noise ratios (SNRs). The image sharpness, spatial resolution, and contrast between tissues were analyzed and compared with other CSA techniques. Compared to CSA with multiple acquisition (CSAM) at 25%, 35%, and 45% undersampling, the reduction rates of the k-space lines of CSA with keyhole (CSAK) were 10%, 15%, and 22%, respectively, and the acquisition time was reduced by 16%, 23%, and 32%, respectively. In the simulation performed with a full sampling k-space dataset, the SNR decreased to 10.41, 9.80, and 8.86 in the white matter and 9.69, 9.35, and 8.46 in the gray matter, respectively. In addition, the ringing artifacts became substantially more predominant as the number of sampling lines decreased. The 50% modulation transfer functions were 0.38, 0.43, and 0.54 line pairs per millimeter for CSAM, CSAK with high-frequency sharing (CSAKS), and CSAK with high-frequency copying (CSAKC), respectively. In this study, we demonstrated that the smaller the sampling line, the more severe the ringing artifact, and that the CSAKC technique proposed to overcome the artifacts that occur when using CSA techniques did not generate artifacts, while it increased spatiotemporal resolution.

Fourier transform-based diffraction calculations are essential for computational optics. However, the diffraction calculations can be corrupted by the introduction of strong ringing artifacts due to the introduction of zero-padding to avoid circular convolution or to control the sampling intervals. We propose a simple de-ringing method using average subtractions for application to on-axis and off-axis diffraction calculations. To verify the effectiveness of the proposed method, we compared the diffracted fields obtained using zero-padding, a flat-top window method, a mirror expansion method, and the whole and border average subtractions proposed. Furthermore, we confirmed the effectiveness of the proposed method for hologram calculations using double phase encoding and image reconstructions of inline digital holography.

We propose a superresolution (SR) algorithm based on adaptive sparse representation via multiple dictionaries for images taken by unmanned aerial vehicles (UAVs). The SR attainable through the proposed algorithm can increase the precision of 3-D reconstruction from UAV images, enabling the production of high-resolution images for constructing high-frequency time series and for high-precision digital mapping in agriculture. The basic idea of the proposed method is to use a field server or ground-based camera to take training images and then construct multiple pairs of dictionaries based on selective sparse representations to reduce instability during the sparse coding process. The dictionaries are classified on the basis of the edge orientation into five clusters: 0, 45, 90, 135, and nondirection. The proposed method is expected to reduce blurring, blocking, and ringing artifacts especially in edge areas. We evaluated the proposed and previous methods using peak signal-to-noise ratio, structural similarity, feature similarity, and computation time. Our experimental results indicate that the proposed method clearly outperforms other state-of-the-art algorithms based on qualitative and quantitative analysis. In the end, we demonstrate the effectiveness of our proposed method to increase the precision of 3-D reconstruction from UAV images.

Total variation (TV) regularization has proven effective for a range of computer vision tasks through its preferential weighting of sharp image edges. Existing TV-based methods, however, often suffer from the over-smoothing issue and solution bias caused by the homogeneous penalization. In this paper, we consider addressing these issues by applying inhomogeneous regularization on different image components. We formulate the inhomogeneous TV minimization problem as a convex quadratic constrained linear programming problem. Relying on this new model, we propose a matching pursuit-based total variation minimization method (MPTV), specifically for image deconvolution. The proposed MPTV method is essentially a cutting-plane method that iteratively activates a subset of nonzero image gradients and then solves a subproblem focusing on those activated gradients only. Compared with existing methods, the MPTV is less sensitive to the choice of the trade-off parameter between data fitting and regularization. Moreover, the inhomogeneity of MPTV alleviates the over-smoothing and ringing artifacts and improves the robustness to errors in blur kernel. Extensive experiments on different tasks demonstrate the superiority of the proposed method over the current state of the art.

Gibbs-ringing artifact is a common artifact in MRI image processing. As MRI raw data is taken in a frequency domain, 2D in- verse discrete Fourier transform is applied to visualize data. Inability to take inverse Fourier transform of full spectrum (full k-space) leads to the insufficient sampling of the high frequency data and results in a well-known Gibbs phenomenon. It is worth to notice that truncation of high frequency information generates a significant blur, thus some techniques from other image restoration problems (for example, image deblur task) can be successfully used. We propose attention-based convolutional neural network for Gibbs-ringing reduction which is the extension of recently proposed GAS-CNN (Gibbs-ringing Artifact Suppression Convolutional Neural Network). Proposed method includes simplified non-linear mapping, amended by LRNN (Layer Recurrent Neural Network) refinement block with feature attention module, controlling the correlation between input and output tensors of the refinement unit. The research shows that the proposed post-processing refinement construction considerably simplifies the non-linear mapping.

The region of image compression since it is applicable to various fields of image processing. The key goal of compression is to cut back the space for storage and decrease the transmission cost and maintain the visual quality of image. This paper presents fundamental approach for analysis and evaluating the image compression techniques. This paper describes the many techniques which can be used for image compression and describes about artifacts which can be consequence of compression and their reduction techniques. This paper presents a new Canny edge based restoration method to remove artefacts from compressed image. Firstly standard JPEG compression is used to compress the first image. Compressed image contains various visible artefacts like blurring artifacts, blocking artifacts and ringing artifacts. Then to remove ringing artifacts proposed method i.e. image restoration as a post processing method is used. Experimental effects of proposed method by use of various performance parameters are good. General Terms Image Compression and reduction of compression artifacrs

Image restoration is widely applied in many areas. When operating on images with different scales for the representation of pixel intensity levels or low SNR, the traditional restoration algorithm lacks validity and induces noise amplification, ringing artifacts and poor convergent ability. In this paper, an improved NAS-RIF algorithm is proposed to overcome the shortcomings of the traditional algorithm. The improved algorithm proposes a solution for blurred with noise image by constrained maximization of some of the detail wavelet packet energies. This algorithm gives enhancement with the sharpness of the deconvolved images. In determining the support region, a presegmentation is used to form it close to the object in the image, Moreover, as compared with the traditional algorithm. Simulations show that the improved algorithm behaves a better convergence, noise resistance and provides a better estimate of the original image.

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.

Purpose To develop a fast and stable method for correcting the gibbs-ringing artifact. Methods Gibbs-ringing is a well-known artifact which manifests itself as spurious oscillations in the vicinity of sharp image gradients at tissue boundaries. The origin can be seen in the truncation of k-space during MRI data-acquisition. Correction techniques like Gegenbauer reconstruction or extrapolation methods aim at recovering these missing data. Here, we present a simple and robust method which exploits a different view on the Gibbs-phenomenon: The truncation in k-space can be interpreted as a convolution of the underlying image with a sinc-function. As the image is reconstructed on a discretized grid, the severity of the ringing artifacts depends on how this grid is located with respect to the edge and the oscillation pattern of the function. We propose to reinterpolate the image based on local, subvoxel-shifts to sample the ringing pattern at the zero-crossings of the oscillating sinc-function. Results With the proposed method, the artifact can simply, effectively, and robustly be removed with a minimal amount of image smoothing. Conclusions The robustness of the method suggests it as a suitable candidate for an implementation in the standard image processing pipeline in clinical routine. Magn Reson Med 76:1574-1581, 2016. © 2015 International Society for Magnetic Resonance in Medicine.

Blurring is a common artifact that produces distorted images with unavoidable information loss. The Blind image deconvolution is to recover the sharp estimate of a given blurry image when the blur kernel is unknown. Despite the availability of deconvolution methods, it is still uncertain how to regularize the blur kernel in an effectual fashion which could substantially improve the results even when the image is blurred to its extend. This paper presents a novel deconvolution method that describes an efficient optimization scheme that alternates between estimation of blur kernel and restoration of sharp image until convergence. The system engenders a more efficient regularizer for the blur kernel that can generally and considerably benefit the solution for the problem of blind deconvolution. Also the blur metric concept in the system provides an automated environment for the selection of deconvolutoin parameters. The outlier handling model used in this work detects and eliminates the major causes of visual artifacts. As a result the system produces high quality deblurred results that preserves fine edge details of an image and complex image structures, while avoiding visual artifacts. The experiments on realistic images show that the proposed deconvolution method can produce high quality deblurred images with very little ringing artifacts even when the image is severely blurred, and the ability of system in choosing the appropriate input parameters for deconvolution. General Terms Image processing, signal processing, blind and non blind image debluring

Undersampling in Fourier single pixel imaging (FSI) is often employed to reduce imaging time for real-time applications. However, the undersampled reconstruction contains ringing artifacts (Gibbs phenomenon) that occur because the high-frequency target information is not recorded. Furthermore, by employing 3-step FSI strategy (reduced measurements with low noise suppression) with a low-grade sensor (i.e., photodiode), this ringing is coupled with noise to produce unwanted artifacts, lowering image quality. To improve the imaging quality of real-time FSI, a fast image reconstruction framework based on deep convolutional autoencoder network (DCAN) is proposed. The network through context learning over FSI artifacts is capable of deringing, denoising, and recovering details in 256 × 256 images. The promising experimental results show that the proposed deep-learning-based FSI outperforms conventional FSI in terms of image quality even at very low sampling rates (1-4%).