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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

Suppression of ringing artifacts in images is a well-known image restoration problem. Gibbs-ringing artifacts occur when, in the process of magnetic resonance imaging, the source data from the frequency domain are mapped onto the spatial domain by using the discrete Fourier transform. The artifacts are caused by the incompleteness of these data, which, in turn, is due to cutting off the high frequencies of the Fourier spectrum. In this paper, we propose a hybrid method for Gibbs-ringing artifact suppression in magnetic resonance images that combines a deep learning model and a classical non-machine-learning algorithm for Gibbs-ringing artifact suppression based on optimal subvoxel shifts.

Purpose To develop a true single shot turbo spin echo (SShTSE) acquisition with Dixon for robust T2 -weighted abdominal imaging with uniform fat and water separation at 3T. Methods The in-phase (IP) and out-of-phase (OP) echoes for Dixon processing were acquired in the same repetition time of a SShTSE using partial echoes. A phase-preserved bi-directional homodyne reconstruction was developed to compensate the partial echo and the partial phase encoding of SShTSE. With IRB approval, the SShTSE-Dixon was compared against standard SShTSE, without and with fat suppression using spectral adiabatic inversion recovery (SPAIR) in 5 healthy volunteers and 5 patients. The SNR and contrast ratio (CR) of spleen to liver were compared among different acquisitions. Results The bi-directional homodyne reconstruction successfully minimized ringing artifacts because of partial acquisitions. SShTSE-Dixon achieved uniform fat suppression compared to SShTSE-SPAIR with fat suppression failures of 1/10 versus 10/10 in the axial plane and 0/5 versus 5/5 in the coronal plane, respectively. The SNRs of the liver (12.2 ± 4.9 vs. 11.7 ± 5.2; P = .76) and spleen (25.9 ± 11.6 vs. 23.7 ± 9.7; P = .14) were equivalent between fat-suppressed images (SShTSE-Dixon water-only and SShTSE-SPAIR). The SNRs of liver (14.4 ± 5.7 vs. 13.4 ± 5.0; P = .60) and spleen (26.5 ± 10.1 vs. 25.7 ± 8.5; P = .56) were equivalent between non-fat-suppressed images (SShTSE-Dixon IP and SShTSE). The CRs of spleen to liver were also similar between fat-suppressed images (2.6 ± 0.4 vs. 2.5 ± 0.5; P =.92) and non-fat-suppressed images (2.3 ± 0.6 vs. 2.2 ± 0.4; P =.84). Conclusion SShTSE-Dixon generates robust abdominal T2 -weighted images at 3T with and without uniform fat suppression, along with perfectly co-registered fat-only images in a single acquisition.

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

This article presents an efficient superblock-based sample adaptive offset ( superSAO ) that jointly exploits block-wise filter partition and probabilistic band interval segmentation to improve quality-of-experience of industrial video applications. Specifically, we investigate the partition flexibility of a superSAO block whose root size is up to 256 × 256, and propose to optimize the block-wise SAO filter by considering computation complexity and compression efficiency. Furthermore, we segment the band interval by equal probability of the sample intensity distributions, which facilitates the computation of better band offsets to attenuate ringing artifact due to quantization errors or encoded motion vectors. Experimental results show that the proposed superSAO method outperforms state-of-the-art approaches by obtaining 4.6% bandwidth reduction on average for the low delay and high-compression video applications.

Ringing artifacts are the main noise sources in holographic projection when the quadratic phase is introduced to suppress speckle noise. In this study, the mechanisms of ringing artifacts on reconstructed images are theoretically analyzed, which illustrates the ringing artifacts are related to the bandwidth properties of the reconstructed wave field. Based on the frequency analysis, a band-limited iterative algorithm is proposed to optimize the phase hologram in the Fresnel domain. The proposed method can effectively suppress the ringing artifacts as well as the speckle noise of the Fresnel hologram by optimizing the phase distribution with bandwidth constraint. Numerical simulations and optical experiments have been performed to validate the proposed method for providing quality reconstructions in lensless holographic projection.

Face images deblurring has achieved advanced development; however, existing methods involve high computational cost problems. Furthermore, the recovered face images by current methods have the problems of over-smooth textures, ringing artifacts, and poor details. We consider the problem of face images deblurring as a semantic generation task. In this paper, we propose a generative adversarial network (GAN), which includes a perception-inspired blurry removal generator and a discriminator. The proposed generator reconstructs the latent deblurred image by a U-net based network that contains an enhancement module. Face images are highly structured, and thus can be served as a class-specific prior. Considering this, we propose a perceptual loss function to regularize the recovery of face images, which introduces more clear details and reduces the effects of artifacts. The proposed method has a robust capability of generating realistic face images with pleasant visual effects. Extensive experiments on both synthetic and real-world face images demonstrate that the proposed method is comparable with state-of-the-art methods.

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.

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

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.

Gibbs-ringing is a common artifact in magnetic resonance imaging (MRI), which is mainly caused by the finite k-space sampling and the truncation of high frequency (HF) information at the sampling border. It is especially visible for imaging acquisitions at low resolution and can be typically suppressed by filtering at the expense of further loss of HF components. As a classic image restoration problem in MRI, Gibbs-ringing artifact suppression can be viewed as a typical ill-posed inverse problem of image generation in computer vision community, such as image super-resolution and inpainting. Inspired by this, the present work presents a novel method to suppress the Gibbs-ringing artifacts with knowledge transfer from natural images to MR images. The highly nonlinear relation between the artifact-degraded image and the corresponding artifact-free counterpart is modeled with a typical convolutional neural network (CNN), which is first trained with natural images and then fine-tuned with MR images. Unlike many other works, we use transfer learning between different types of images to deal with regression problems, rather than classification problems. The experimental results exhibit that there exists information sharing between natural images and MR images with regard to the same problem, and the knowledge learned from natural images can indeed improve the performance of regression models on MR images.

International audience; Estimating the space-charge distribution using the pulsed electro-acoustic method requires a calibration procedure, in which a reference voltage signal is measured and used to divide the output voltage signal of the piezoelectric sensor. Such a procedure is time consuming and generally involves manually setting filter parameters whose values should be chosen to reduce ringing artifacts in the charge profile and, at the same time, preserve the bandwidth of the profile. This paper proposes a model-based estimation method called the matrix pencil that does not require a calibration procedure to retrieve the charge distribution, albeit with normalized amplitudes, and thus it paves the way for reducing data acquisition and processing times. Results on simulated and experimental data confirm the effectiveness of the proposed method in estimating the space-charge distribution in irradiated polymers.; L'estimation de la distribution de charge d'espace à l'aide de la méthode électroacoustique pulsée nécessite une procédure d'étalonnage, dans laquelle un signal de tension de référence est mesuré et utilisé pour diviser le signal de tension de sortie du capteur piézoélectrique. Une telle procédure est longue et implique généralement de régler manuellement des paramètres de filtre dont les valeurs doivent être choisies pour réduire les artefacts dans le profil de charge et, en même temps, préserver la bande passante du profil. Cet article propose une méthode d'estimation basée sur un modèle appelée le crayon matriciel qui ne nécessite pas de procédure d'étalonnage pour récupérer la distribution de charge, bien qu'avec des amplitudes normalisées, et ouvre ainsi la voie à la réduction des temps d'acquisition et de traitement des données. Les résultats sur les données simulées et expérimentales confirment l'efficacité de la méthode proposée pour estimer la distribution de charge d'espace dans les polymères irradiés.

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.

Photonics Integrated Interference Imaging System (PIIIS) is a recently developed far-filed imaging approach aiming at high imaging performance and compact structure. Several sampling lenslet arrays have been proposed to acquire higher imaging quality. These lens configurations provide low-frequency intensive but high-frequency sparse measurements of complex visibility. However, this kind of sampling will induce Gibbs-ringing artifacts in inverse Fourier images. In this article, entropy prior and smoothness prior are proposed to remove the ringing artifacts, and stabilized CLEAN algorithms based on those priors are used to reconstruct images. A simulation experiment using hierarchical multistage sampling lens array is implemented, and the results show that these algorithms can eliminate the ringing artifacts.

Snapshot Compressive Imaging (SCI) systems, including hyperspectral compressive imaging and video compressive imaging, are designed to depict high-dimensional signals with limited data by mapping multiple images into one. One key module of SCI systems is a high quality reconstruction algorithm for original frames. However, most existing decoding algorithms are based on vectorization representation and fail to capture the intrinsic structural information of high dimensional signals. In this paper, we propose a tensor-based low-rank reconstruction algorithm with hyper-Laplacian constraint for hyperspectral SCI systems. First, we integrate the non-local self-similarity and tensor low-rank minimization approach to explore the intrinsic structural correlations along spatial and spectral domains. Then, we introduce a hyper-Laplacian constraint to model the global spectral structures, alleviating the ringing artifacts in the spatial domain. Experimental results on hyperspectral image corpus demonstrate the proposed algorithm achieves average 0.8~2.9 dB improvement in PSNR over state-of-the-art work.

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.

Purpose To develop a machine learning approach using convolutional neural network for reducing MRI Gibbs-ringing artifact. Theory and methods Gibbs-ringing artifact in MR images is caused by insufficient sampling of the high frequency data. Existing methods exploit smooth constraints to reduce intensity oscillations near sharp edges at the cost of blurring details. In this work, we developed a machine learning approach for removing the Gibbs-ringing artifact from MR images. The ringing artifact was extracted from the original image using a deep convolutional neural network and then subtracted from the original image to obtain the artifact-free image. Finally, its low-frequency k-space data were replaced with measured counterparts to enforce data fidelity further. We trained the convolutional neural network using 17,532 T2-weighted (T2W) normal brain images and evaluated its performance on T2W images of normal and tumor brains, diffusion-weighted brain images, and T2W knee images. Results The proposed method effectively removed the ringing artifact without noticeable smoothing in T2W and diffusion-weighted images. Quantitatively, images produced by the proposed method were closer to the fully-sampled reference images in terms of the root-mean-square error, peak signal-to-noise ratio, and structural similarity index, compared with current state-of-the-art methods. Conclusion The proposed method presents a novel and effective approach for Gibbs-ringing reduction in MRI. The convolutional neural network-based approach is simple, computationally efficient, and highly applicable in routine clinical MRI.

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.

Blind image deblurring tries to restore a blurred image to a clear image without the blurring kernel known in advance, which is widely required in applications such as computer vision and medical image processing. With regard to this, the key issues here are to accurately estimate the blurring kernel for deconvolution of a blurred image, and avoid the ringing artifacts in the restored image, which are both related to high-quality detection of edge information in the blurred image. Though much endeavor has been made, it is still difficult to extract edge information well in blurred images and lacks investigation how edge information causes ringing artifacts. In this paper, we make a study on this and develop novel measures to optimize edge extraction and determine suitable width and weights for the extracted edges for reinforcing their use in deblurring, by which image deblurring can be improved with ringing artifacts considerably suppressed. Experimental results demonstrate our improvements over the existing methods.

H.265/HEVC (high efficiency video coding) adopts in-loop filters to reduce artifacts such as blocking artifacts, ringing artifacts, but those above-mentioned artifacts are still obvious in the case of large quantization step. This paper presents a novel in-loop filtering mechanism based on three-dimensional (3D) sub-bands in adaptive group of frames and convolutional neural network (CNN) models for further improvement of in-loop filtering capability. Firstly, the video frame sequence is adaptively grouped; then, a group of video frames is decomposed into frequency sub-bands by 3D wavelet transform; afterward, frequency sub-bands with different types are filtered by corresponding CNN model; finally, the group of video frames is synthesized by 3D wavelet inverse transform. We apply the nonlinear mapping ability of CNN into the modification of wavelet coefficients. The sub-bands of high frequency and low frequency are filtered by four offline CNN models with different directional characteristics trained with training data generated by 3D wavelet transform, respectively. The details of the high frequency part are enhanced, while the quality of low-pass image is promoted, so that those artifacts can be effectively alleviated. By means of comparing experiments, the subjective and objective results show that our proposed method has better filtering performance than the in-loop filtering mechanism in HM16.18; especially the video frame quality can be effectively improved in the case of large quantization step.

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.

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.

In this paper, we propose high quality image debluring by using discrete cosine transform (DCT) with less computational complexity. Recently, Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) based algorithms have been proposed for image deblurring. Moreover, multi-scale architecture of CNN restores blurred image cleary and suppresses more ringing artifacts or block noise, but it takes much time to process. To solve these problems, we propose a method that preserves texture and suppresses ringing artifacts in the restored image without multi-scale architecture using DCT based loss named “DeblurDCTGAN.”. It compares frequency domain of the images made from deblurred image and ground truth image by using DCT. Hereby, DeblurDCTGAN can reduce block noise or ringing artifacts while maintaining deblurring performance. Our experimental results show that DeblurDCTGAN gets the highest performances on both PSNR and SSIM comparing with other conventional methods in GoPro, DVD, NFS and HIDE test Dataset. Also, the running time per pair of DeblurDCTGAN is faster than others.

Images acquired with standard digital cameras have Bayer patterns and suffer from lens blur. A demosaicking step is implemented in every digital camera, yet blur often remains unattended due to computational cost and instability of deblur-ring algorithms. Linear methods, which are computationally less demanding, produce ringing artifacts in deblurred images. Complex non-linear deblurring methods avoid artifacts, however their complexity imply offline application after camera demosaicking, which leads to sub-optimal performance. In this work, we propose a joint demosaicking deblurring and deringing network with a light-weight architecture inspired by the alternating direction method of multipliers. The proposed network has a transparent and clear interpretation compared to other black-box data driven approaches. We experimentally validate its superiority over state-of-the-art demosaicking methods with offline deblurring.