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 726–750 of 1459 papers

TitleStatusHype
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
The Neural Tangent Link Between CNN Denoisers and Non-Local Filters—0
CoDe: An Explicit Content Decoupling Framework for Image Restoration—0
Coercive functions from a topological viewpoint and properties of minimizing sets of convex functions appearing in image restoration—0
Color Correction Meets Cross-Spectral Refinement: A Distribution-Aware Diffusion for Underwater Image Restoration—0
Color graph based wavelet transform with perceptual information—0
Complexity Experts are Task-Discriminative Learners for Any Image Restoration—0
Compression, Restoration, Re-sampling, Compressive Sensing: Fast Transforms in Digital Imaging—0
Compressive Shack-Hartmann Wavefront Sensor based on Deep Neural Networks—0
Computationally iterative methods for salt-and-pepper denoising—0
Concatenated Attention Neural Network for Image Restoration—0
Concept for a CMOS Image Sensor Suited for Analog Image Pre-Processing—0
Conformal and Low-Rank Sparse Representation for Image Restoration—0
Consistency Posterior Sampling for Diverse Image Synthesis—0
Consistent Diffusion: Denoising Diffusion Model with Data-Consistent Training for Image Restoration—0
Content-adaptive Representation Learning for Fast Image Super-resolution—0
Content-Aware Depth-Adaptive Image Restoration—0
Controllable Image Restoration for Under-Display Camera in Smartphones—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