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 901–925 of 1459 papers

TitleStatusHype
Blur, Noise, and Compression Robust Generative Adversarial Networks—0
Boosted GAN with Semantically Interpretable Information for Image Inpainting—0
Boosting Diffusion-Based Text Image Super-Resolution Model Towards Generalized Real-World Scenarios—0
Boosting Image Restoration via Priors from Pre-trained Models—0
Boosting ViT-based MRI Reconstruction from the Perspectives of Frequency Modulation, Spatial Purification, and Scale Diversification—0
BP-DIP: A Backprojection based Deep Image Prior—0
Breaking Complexity Barriers: High-Resolution Image Restoration with Rank Enhanced Linear Attention—0
Bregman Iteration for Correspondence Problems: A Study of Optical Flow—0
A Benchmark for Sparse Coding: When Group Sparsity Meets Rank Minimization—0
Bridge the Gap between SNN and ANN for Image Restoration—0
Bridging the Gap Between Computational Photography and Visual Recognition—0
Building Bridge Across the Time: Disruption and Restoration of Murals In the Wild—0
Burst Image Deblurring Using Permutation Invariant Convolutional Neural Networks—0
Can fully convolutional networks perform well for general image restoration problems?—0
Cascades of Regression Tree Fields for Image Restoration—0
CasSR: Activating Image Power for Real-World Image Super-Resolution—0
Cat-AIR: Content and Task-Aware All-in-One Image Restoration—0
CDI: Blind Image Restoration Fidelity Evaluation based on Consistency with Degraded Image—0
Circular Image Deturbulence using Quasi-conformal Geometry—0
Clarity ChatGPT: An Interactive and Adaptive Processing System for Image Restoration and Enhancement—0
ClassPruning: Speed Up Image Restoration Networks by Dynamic N:M Pruning—0
CLEAR: Covariant LEAst-square Re-fitting with applications to image restoration—0
Clear Nights Ahead: Towards Multi-Weather Nighttime Image Restoration—0
Generalizable Denoising of Microscopy Images using Generative Adversarial Networks and Contrastive Learning—0
CLII: Visual-Text Inpainting via Cross-Modal Predictive Interaction—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