{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/image-restoration/papers/14","list_of":"/task/image-restoration","task":"Image Restoration","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":14,"pages_in_order":15,"rows_per_page":100,"rows":[1301,1400],"of":1459,"counts":{"archive_papers_tagged":1459,"with_a_code_link":666,"where_syntology_ran_a_sample":175,"not_listed_spam_title":0,"listed":1459,"listed_where_code_ran":175,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":148,"every_run_a_failure_of_syntologys_instrument":27,"listed_with_a_run_with_no_instrument_failure":148,"listed_every_run_a_failure_of_syntologys_instrument":27,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/image-restoration","prev":"/task/image-restoration/papers/13","next":"/task/image-restoration/papers/15","papers":[{"url":null,"slug":"generalization-of-the-dark-channel-prior-for","title":"Generalization of the Dark Channel Prior for Single Image Restoration","date":"2019-03-07","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"image-restoration-by-combined-order","title":"Image Restoration by Combined Order Regularization with Optimal Spatial Adaptation","date":"2019-03-07","arxiv_id":"1903.03133","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-action-recognition-using","title":"Privacy-Preserving Action Recognition using Coded Aperture Videos","date":"2019-02-25","arxiv_id":"1902.09085","repositories_listed":0,"syntology":null},{"url":null,"slug":"bridging-the-gap-between-computational","title":"Bridging the Gap Between Computational Photography and Visual Recognition","date":"2019-01-28","arxiv_id":"1901.09482","repositories_listed":0,"syntology":null},{"url":null,"slug":"linearized-admm-and-fast-nonlocal-denoising","title":"Linearized ADMM and Fast Nonlocal Denoising for Efficient Plug-and-Play Restoration","date":"2019-01-18","arxiv_id":"1901.06110","repositories_listed":0,"syntology":null},{"url":null,"slug":"i-can-see-clearly-now-image-restoration-via","title":"I Can See Clearly Now : Image Restoration via De-Raining","date":"2019-01-03","arxiv_id":"1901.00893","repositories_listed":0,"syntology":null},{"url":null,"slug":"total-variation-with-overlapping-group-1","title":"Total Variation with Overlapping Group Sparsity and Lp Quasinorm for Infrared Image Deblurring under Salt-and-Pepper Noise","date":"2018-12-31","arxiv_id":"1812.11725","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimation-and-restoration-of-compositional","title":"Estimation and Restoration of Compositional Degradation Using Convolutional Neural Networks","date":"2018-12-23","arxiv_id":"1812.09629","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolutionary-neural-architecture-search-for","title":"Evolutionary Neural Architecture Search for Image Restoration","date":"2018-12-14","arxiv_id":"1812.05866","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-psychovisual-analysis-on-deep-cnn-features","title":"Why Are Deep Representations Good Perceptual Quality Features?","date":"2018-12-02","arxiv_id":"1812.00412","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-with-inaccurate-training-data","title":"Deep Learning with Inaccurate Training Data for Image Restoration","date":"2018-11-18","arxiv_id":"1811.07268","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-attention-network-for-low-dose-ct","title":"Visual Attention Network for Low Dose CT","date":"2018-10-31","arxiv_id":"1810.13059","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-restoration-using-total-variation","title":"Image Restoration using Total Variation Regularized Deep Image Prior","date":"2018-10-30","arxiv_id":"1810.12864","repositories_listed":0,"syntology":null},{"url":null,"slug":"dn-resnet-efficient-deep-residual-network-for","title":"DN-ResNet: Efficient Deep Residual Network for Image Denoising","date":"2018-10-16","arxiv_id":"1810.06766","repositories_listed":0,"syntology":null},{"url":null,"slug":"weighted-sigmoid-gate-unit-for-an-activation","title":"Weighted Sigmoid Gate Unit for an Activation Function of Deep Neural Network","date":"2018-10-03","arxiv_id":"1810.01829","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-image-restoration","title":"Active image restoration","date":"2018-09-22","arxiv_id":"1809.08406","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-blind-image-restoration-based-on","title":"Non-blind Image Restoration Based on Convolutional Neural Network","date":"2018-09-11","arxiv_id":"1809.03757","repositories_listed":0,"syntology":null},{"url":null,"slug":"burst-image-deblurring-using-permutation","title":"Burst Image Deblurring Using Permutation Invariant Convolutional Neural Networks","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reconstruction-based-pairwise-depth-dataset","title":"Reconstruction-based Pairwise Depth Dataset for Depth Image Enhancement Using CNN","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-analysis-of-plug-and-play-admm-a","title":"Performance Analysis of Plug-and-Play ADMM: A Graph Signal Processing Perspective","date":"2018-08-31","arxiv_id":"1809.00020","repositories_listed":0,"syntology":null},{"url":null,"slug":"ct-super-resolution-gan-constrained-by-the","title":"CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)","date":"2018-08-10","arxiv_id":"1808.04256","repositories_listed":0,"syntology":null},{"url":null,"slug":"x-gans-image-reconstruction-made-easy-for","title":"X-GANs: Image Reconstruction Made Easy for Extreme Cases","date":"2018-08-06","arxiv_id":"1808.04432","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-power-of-complementary-regularizers-image","title":"The Power of Complementary Regularizers: Image Recovery via Transform Learning and Low-Rank Modeling","date":"2018-08-03","arxiv_id":"1808.01316","repositories_listed":0,"syntology":null},{"url":null,"slug":"physics-based-generative-adversarial-models","title":"Physics-Based Generative Adversarial Models for Image Restoration and Beyond","date":"2018-08-02","arxiv_id":"1808.00605","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-bin-trainable-linear-unit-for-fast","title":"Multi-bin Trainable Linear Unit for Fast Image Restoration Networks","date":"2018-07-30","arxiv_id":"1807.11389","repositories_listed":0,"syntology":null},{"url":null,"slug":"linkage-between-piecewise-constant-mumford","title":"Linkage between piecewise constant Mumford-Shah model and ROF model and its virtue in image segmentation","date":"2018-07-26","arxiv_id":"1807.10194","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-hybrid-sparsity-prior-for-image","title":"Learning Hybrid Sparsity Prior for Image Restoration: Where Deep Learning Meets Sparse Coding","date":"2018-07-18","arxiv_id":"1807.06920","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-generic-diffusion-processes-for","title":"Learning Generic Diffusion Processes for Image Restoration","date":"2018-07-17","arxiv_id":"1807.06216","repositories_listed":0,"syntology":null},{"url":null,"slug":"external-patch-based-image-restoration-using","title":"External Patch-Based Image Restoration Using Importance Sampling","date":"2018-07-09","arxiv_id":"1807.03018","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-restoration-using-conditional-random","title":"Image Restoration Using Conditional Random Fields and Scale Mixtures of Gaussians","date":"2018-07-09","arxiv_id":"1807.03027","repositories_listed":0,"syntology":null},{"url":null,"slug":"vehicle-image-generation-going-well-with-the","title":"Vehicle Image Generation Going Well with The Surroundings","date":"2018-07-09","arxiv_id":"1807.02925","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-adversarial-networks-and","title":"Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution","date":"2018-06-14","arxiv_id":"1806.05764","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-scale-weighted-nuclear-norm-image","title":"Multi-Scale Weighted Nuclear Norm Image Restoration","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"image-restoration-by-estimating-frequency","title":"Image Restoration by Estimating Frequency Distribution of Local Patches","date":"2018-05-23","arxiv_id":"1805.09097","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamically-unfolding-recurrent-restorer-a","title":"Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration","date":"2018-05-20","arxiv_id":"1805.07709","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-tracking-with-correlation-filters","title":"Object Tracking with Correlation Filters using Selective Single Background Patch","date":"2018-05-09","arxiv_id":"1805.03453","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-scale-face-restoration-with-sequential","title":"Multi-Scale Face Restoration with Sequential Gating Ensemble Network","date":"2018-05-06","arxiv_id":"1805.02164","repositories_listed":0,"syntology":null},{"url":null,"slug":"densely-connected-high-order-residual-network","title":"Densely Connected High Order Residual Network for Single Frame Image Super Resolution","date":"2018-04-16","arxiv_id":"1804.05902","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-convolutional-sparse-coding","title":"Supervised Convolutional Sparse Coding","date":"2018-04-08","arxiv_id":"1804.02678","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-cascaded-convolutional-neural-network-for","title":"A Cascaded Convolutional Neural Network for Single Image Dehazing","date":"2018-03-21","arxiv_id":"1803.07955","repositories_listed":0,"syntology":null},{"url":null,"slug":"nonlocal-low-rank-tensor-factor-analysis-for","title":"Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration","date":"2018-03-19","arxiv_id":"1803.06795","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-based-dequantization-for-image","title":"Learning-Based Dequantization For Image Restoration Against Extremely Poor Illumination","date":"2018-03-05","arxiv_id":"1803.01532","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-denoising-with-generalized-gaussian","title":"Image denoising with generalized Gaussian mixture model patch priors","date":"2018-02-05","arxiv_id":"1802.01458","repositories_listed":0,"syntology":null},{"url":null,"slug":"near-lossless-l-infinity-constrained-multi","title":"Near-lossless $\\ell_\\infty$-constrained Image Decompression via Deep Neural Network","date":"2018-01-18","arxiv_id":"1801.07987","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-spectral-image-processing","title":"Graph Spectral Image Processing","date":"2018-01-16","arxiv_id":"1801.04749","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-blind-image-inpainting","title":"Deep Blind Image Inpainting","date":"2017-12-25","arxiv_id":"1712.09078","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhance-visual-recognition-under-adverse","title":"Enhance Visual Recognition under Adverse Conditions via Deep Networks","date":"2017-12-20","arxiv_id":"1712.07732","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-gmm-based-patch-priors-for-image","title":"Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100$\\times$ speed-up","date":"2017-10-23","arxiv_id":"1710.08124","repositories_listed":0,"syntology":null},{"url":"/paper/ug2-a-video-benchmark-for-assessing-the","slug":"ug2-a-video-benchmark-for-assessing-the","title":"UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition","date":"2017-10-09","arxiv_id":"1710.02909","repositories_listed":0,"syntology":null},{"url":null,"slug":"depth-and-image-restoration-from-light-field","title":"Depth and Image Restoration From Light Field in a Scattering Medium","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-discriminative-data-fitting","title":"Learning Discriminative Data Fitting Functions for Blind Image Deblurring","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-super-resolve-blurry-face-and","title":"Learning to Super-Resolve Blurry Face and Text Images","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bridge-the-gap-between-group-sparse-coding","title":"A Benchmark for Sparse Coding: When Group Sparsity Meets Rank Minimization","date":"2017-09-12","arxiv_id":"1709.03979","repositories_listed":0,"syntology":null},{"url":null,"slug":"weighted-low-rank-tensor-recovery-for","title":"Weighted Low-rank Tensor Recovery for Hyperspectral Image Restoration","date":"2017-09-01","arxiv_id":"1709.00192","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-wavelet-frame-coefficient-total-variational","title":"A wavelet frame coefficient total variational model for image restoration","date":"2017-08-25","arxiv_id":"1708.07601","repositories_listed":0,"syntology":null},{"url":null,"slug":"structure-preserving-image-super-resolution","title":"Structure-Preserving Image Super-resolution via Contextualized Multi-task Learning","date":"2017-07-26","arxiv_id":"1707.08340","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-adversarial-network-based-on","title":"Generative Adversarial Network based on Resnet for Conditional Image Restoration","date":"2017-07-16","arxiv_id":"1707.04881","repositories_listed":0,"syntology":null},{"url":null,"slug":"impulsive-noise-removal-from-color-images","title":"Impulsive noise removal from color images with morphological filtering","date":"2017-07-11","arxiv_id":"1707.03126","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperspectral-image-restoration-via-total","title":"Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition","date":"2017-07-08","arxiv_id":"1707.02477","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-dynamic-guidance-for-depth-image","title":"Learning Dynamic Guidance for Depth Image Enhancement","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"image-restoration-from-patch-based-compressed","title":"Image Restoration from Patch-based Compressed Sensing Measurement","date":"2017-06-02","arxiv_id":"1706.00597","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-committee-approach-for-image-restoration","title":"Self-Committee Approach for Image Restoration Problems using Convolutional Neural Network","date":"2017-05-12","arxiv_id":"1705.04528","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-convex-weighted-lp-nuclear-norm-based","title":"Non-Convex Weighted Lp Nuclear Norm based ADMM Framework for Image Restoration","date":"2017-04-24","arxiv_id":"1704.07056","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-neural-pyramid-for-image","title":"Convolutional Neural Pyramid for Image Processing","date":"2017-04-07","arxiv_id":"1704.02071","repositories_listed":0,"syntology":null},{"url":null,"slug":"restoration-of-images-with-wavefront","title":"Restoration of Images with Wavefront Aberrations","date":"2017-04-02","arxiv_id":"1704.00331","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-restoration-using-autoencoding-priors","title":"Image Restoration using Autoencoding Priors","date":"2017-03-29","arxiv_id":"1703.09964","repositories_listed":0,"syntology":null},{"url":null,"slug":"discriminative-transfer-learning-for-general","title":"Discriminative Transfer Learning for General Image Restoration","date":"2017-03-27","arxiv_id":"1703.09245","repositories_listed":0,"syntology":null},{"url":null,"slug":"incident-light-frequency-based-image","title":"Incident Light Frequency-based Image Defogging Algorithm","date":"2017-03-03","arxiv_id":"1703.01248","repositories_listed":0,"syntology":null},{"url":null,"slug":"speckle-reduction-with-trained-nonlinear","title":"Speckle Reduction with Trained Nonlinear Diffusion Filtering","date":"2017-02-24","arxiv_id":"1702.07482","repositories_listed":0,"syntology":null},{"url":null,"slug":"manifold-based-low-rank-regularization-for","title":"Manifold Based Low-rank Regularization for Image Restoration and Semi-supervised Learning","date":"2017-02-09","arxiv_id":"1702.02680","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-edge-driven-wavelet-frame-model-for-image","title":"An Edge Driven Wavelet Frame Model for Image Restoration","date":"2017-01-25","arxiv_id":"1701.07158","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-convolutional-network-in-network","title":"A New Convolutional Network-in-Network Structure and Its Applications in Skin Detection, Semantic Segmentation, and Artifact Reduction","date":"2017-01-22","arxiv_id":"1701.06190","repositories_listed":0,"syntology":null},{"url":null,"slug":"shape-estimation-from-defocus-cue-for","title":"Shape Estimation from Defocus Cue for Microscopy Images via Belief Propagation","date":"2016-12-30","arxiv_id":"1612.09411","repositories_listed":0,"syntology":null},{"url":null,"slug":"deeply-aggregated-alternating-minimization","title":"Deeply Aggregated Alternating Minimization for Image Restoration","date":"2016-12-20","arxiv_id":"1612.06508","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-sparse-approximation-for-image","title":"Local Sparse Approximation for Image Restoration with Adaptive Block Size Selection","date":"2016-12-20","arxiv_id":"1612.06738","repositories_listed":0,"syntology":null},{"url":null,"slug":"autoencoder-based-holographic-image","title":"Autoencoder-based holographic image restoration","date":"2016-12-12","arxiv_id":"1612.03959","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-fully-convolutional-networks-perform-well","title":"Can fully convolutional networks perform well for general image restoration problems?","date":"2016-11-14","arxiv_id":"1611.04481","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-variational-bayesian-approach-for-image","title":"A Variational Bayesian Approach for Image Restoration. Application to Image Deblurring with Poisson-Gaussian Noise","date":"2016-10-24","arxiv_id":"1610.07519","repositories_listed":0,"syntology":null},{"url":null,"slug":"near-infrared-coloring-via-a-contrast","title":"Near-Infrared Coloring via a Contrast-Preserving Mapping Model","date":"2016-10-03","arxiv_id":"1610.00382","repositories_listed":0,"syntology":null},{"url":null,"slug":"rain-removal-via-shrinkage-based-sparse","title":"Rain Removal via Shrinkage-Based Sparse Coding and Learned Rain Dictionary","date":"2016-10-03","arxiv_id":"1610.00386","repositories_listed":0,"syntology":null},{"url":null,"slug":"wavelet-based-segmentation-on-the-sphere","title":"Wavelet-Based Segmentation on the Sphere","date":"2016-09-21","arxiv_id":"1609.06500","repositories_listed":0,"syntology":null},{"url":null,"slug":"poisson-noise-reduction-with-higher-order","title":"Poisson Noise Reduction with Higher-order Natural Image Prior Model","date":"2016-09-19","arxiv_id":"1609.05722","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-denoising-via-collaborative-support","title":"Image Denoising Via Collaborative Support-Agnostic Recovery","date":"2016-09-09","arxiv_id":"1609.02932","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-adaptive-parameter-estimation-for-guided","title":"An Adaptive Parameter Estimation for Guided Filter based Image Deconvolution","date":"2016-09-06","arxiv_id":"1609.01380","repositories_listed":0,"syntology":null},{"url":null,"slug":"fractional-calculus-in-image-processing-a","title":"Fractional Calculus In Image Processing: A Review","date":"2016-08-10","arxiv_id":"1608.03240","repositories_listed":0,"syntology":null},{"url":null,"slug":"generic-3d-convolutional-fusion-for-image","title":"Generic 3D Convolutional Fusion for image restoration","date":"2016-07-26","arxiv_id":"1607.07561","repositories_listed":0,"syntology":null},{"url":null,"slug":"clear-covariant-least-square-re-fitting-with","title":"CLEAR: Covariant LEAst-square Re-fitting with applications to image restoration","date":"2016-06-16","arxiv_id":"1606.05158","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-speed-real-time-single-pixel-microscopy","title":"High-speed real-time single-pixel microscopy based on Fourier sampling","date":"2016-06-15","arxiv_id":"1606.05200","repositories_listed":0,"syntology":null},{"url":null,"slug":"simultaneous-inpainting-and-denoising-by","title":"Simultaneous Inpainting and Denoising by Directional Global Three-part Decomposition: Connecting Variational and Fourier Domain Based Image Processing","date":"2016-06-09","arxiv_id":"1606.02861","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-spectral-spatial-correlation-for","title":"Exploiting Spectral-Spatial Correlation for Coded Hyperspectral Image Restoration","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"gradient-domain-image-reconstruction","title":"Gradient-Domain Image Reconstruction Framework With Intensity-Range and Base-Structure Constraints","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-kernel-estimation-with-outliers","title":"Robust Kernel Estimation With Outliers Handling for Image Deblurring","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"image-restoration-with-locally-selected-class","title":"Image Restoration with Locally Selected Class-Adapted Models","date":"2016-05-23","arxiv_id":"1605.07003","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-geometric-approach-to-color-image","title":"A Geometric Approach to Color Image Regularization","date":"2016-05-19","arxiv_id":"1605.05977","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimating-sparse-signals-with-smooth-support","title":"Estimating Sparse Signals with Smooth Support via Convex Programming and Block Sparsity","date":"2016-05-06","arxiv_id":"1605.01813","repositories_listed":0,"syntology":null},{"url":null,"slug":"plug-and-play-admm-for-image-restoration","title":"Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applications","date":"2016-05-05","arxiv_id":"1605.01710","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-backscatter-approximation-for","title":"Effective Backscatter Approximation for Photometry in Murky Water","date":"2016-04-29","arxiv_id":"1604.08789","repositories_listed":0,"syntology":null},{"url":null,"slug":"support-driven-wavelet-frame-based-image","title":"Support Driven Wavelet Frame-based Image Deblurring","date":"2016-03-26","arxiv_id":"1603.08108","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-image-restoration-for-participating","title":"Single Image Restoration for Participating Media Based on Prior Fusion","date":"2016-03-06","arxiv_id":"1603.01864","repositories_listed":0,"syntology":null},{"url":null,"slug":"denoising-and-covariance-estimation-of-single","title":"Denoising and Covariance Estimation of Single Particle Cryo-EM Images","date":"2016-02-22","arxiv_id":"1602.06632","repositories_listed":0,"syntology":null}],"record_sha256":"6106b1c3e73479e5681358653ef8051a67455b78409ea9543444103193a7abf3","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}