SOTAVerified

Image Restoration

Image Restoration is a family of inverse problems for obtaining a high quality image from a corrupted input image. Corruption may occur due to the image-capture process (e.g., noise, lens blur), post-processing (e.g., JPEG compression), or photography in non-ideal conditions (e.g., haze, motion blur).

Source: Blind Image Restoration without Prior Knowledge

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1051–1075 of 1459 papers

TitleStatusHype
Image Inpainting using Partial Convolution—0
High-dimensional Assisted Generative Model for Color Image RestorationCode0
Coupling Model-Driven and Data-Driven Methods for Remote Sensing Image Restoration and Fusion—0
IFR: Iterative Fusion Based Recognizer For Low Quality Scene Text Recognition—0
Deep Camera Obscura: An Image Restoration Pipeline for Lensless Pinhole Photography—0
Rain Removal and Illumination Enhancement Done in One Go—0
Multitask Identity-Aware Image Steganography via Minimax Optimization—0
Attention-Guided Progressive Neural Texture Fusion for High Dynamic Range Image Restoration—0
R3L: Connecting Deep Reinforcement Learning to Recurrent Neural Networks for Image Denoising via Residual Recovery—0
Image restoration quality assessment based on regional differential information entropy—0
HybrUR: A Hybrid Physical-Neural Solution for Unsupervised Underwater Image Restoration—0
A Theory of the Distortion-Perception Tradeoff in Wasserstein Space—0
Low Rank Quaternion Matrix Recovery via Logarithmic Approximation—0
Image Restoration for Remote Sensing: Overview and Toolbox—0
Learning from Pseudo Lesion: A Self-supervised Framework for COVID-19 Diagnosis—0
Controllable Image Restoration for Under-Display Camera in Smartphones—0
Restoring Extremely Dark Images in Real TimeCode0
Zero-Shot Single Image Restoration Through Controlled Perturbation of Koschmieder's Model—0
High-Quality Stereo Image Restoration From Double Refraction—0
Learning a Non-Blind Deblurring Network for Night Blurry Images—0
Patch-Based Image Restoration using Expectation Propagation—0
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images—0
Removal of speckle noises from ultrasound images using five different deep learning networksCode0
Two-stage domain adapted training for better generalization in real-world image restoration and super-resolution—0
Self-Organized Residual Blocks for Image Super-Resolution—0
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All datasetsCDD-11UHDM.

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OneRestoreAverage PSNR (dB)28.72—Unverified
2SRUDCAverage PSNR (dB)27.64—Unverified
3RestormerAverage PSNR (dB)26.99—Unverified
4WGWSNetAverage PSNR (dB)26.96—Unverified
5DGUNetAverage PSNR (dB)26.92—Unverified
6OKNetAverage PSNR (dB)26.33—Unverified
7MIRNetAverage PSNR (dB)25.97—Unverified
8PromptIRAverage PSNR (dB)25.9—Unverified
9MPRNetAverage PSNR (dB)25.47—Unverified
10MIRNetv2Average PSNR (dB)25.37—Unverified
#ModelMetricClaimedVerifiedStatus
1ESDNet-LPSNR22.42—Unverified
2ESDNetPSNR22.12—Unverified
#ModelMetricClaimedVerifiedStatus
1730L37—Unverified