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 401–425 of 1459 papers

TitleStatusHype
Bracketing Image Restoration and Enhancement with High-Low Frequency DecompositionCode2
CU-Mamba: Selective State Space Models with Channel Learning for Image Restoration—0
AdaIR: Exploiting Underlying Similarities of Image Restoration Tasks with Adapters—0
Referring Flexible Image RestorationCode1
Improving Bracket Image Restoration and Enhancement with Flow-guided Alignment and Enhanced Feature Aggregation—0
OmniSSR: Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model—0
Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language ModelsCode4
Equipping Diffusion Models with Differentiable Spatial Entropy for Low-Light Image EnhancementCode1
Joint Conditional Diffusion Model for Image Restoration with Mixed Degradations—0
Rethinking Transformer-Based Blind-Spot Network for Self-Supervised Image DenoisingCode2
Single Stage Adaptive Multi-Attention Network for Image RestorationCode0
Unfolding ADMM for Enhanced Subspace Clustering of Hyperspectral ImagesCode0
STAIC regularization for spatio-temporal image reconstruction—0
Empowering Image Recovery_ A Multi-Attention Approach—0
DiffBody: Human Body Restoration by Imagining with Generative Diffusion Prior—0
Dynamic Pre-training: Towards Efficient and Scalable All-in-One Image RestorationCode2
Flare-Free Vision: Empowering Uformer with Depth InsightsCode1
IPT-V2: Efficient Image Processing Transformer using Hierarchical Attentions—0
GAMA-IR: Global Additive Multidimensional Averaging for Fast Image Restoration—0
Towards Image Ambient Lighting NormalizationCode1
SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational AutoencoderCode0
Self-Rectifying Diffusion Sampling with Perturbed-Attention GuidanceCode3
Serpent: Scalable and Efficient Image Restoration via Multi-scale Structured State Space Models—0
Distilling Semantic Priors from SAM to Efficient Image Restoration Models—0
Omni-Kernel Network for Image RestorationCode2
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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