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 951–975 of 1459 papers

TitleStatusHype
Hybrid Agents for Image Restoration—0
Hybrid Training of Denoising Networks to Improve the Texture Acutance of Digital Cameras—0
Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution—0
HybrUR: A Hybrid Physical-Neural Solution for Unsupervised Underwater Image Restoration—0
HyperKING: Quantum-Classical Generative Adversarial Networks for Hyperspectral Image Restoration—0
Hyper-Restormer: A General Hyperspectral Image Restoration Transformer for Remote Sensing Imaging—0
Hyperspectral Image Restoration and Super-resolution with Physics-Aware Deep Learning for Biomedical Applications—0
Hyperspectral Image Restoration via Global Total Variation Regularized Local nonconvex Low-Rank matrix Approximation—0
Hyperspectral Image Denoising via Multi-modal and Double-weighted Tensor Nuclear Norm—0
Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition—0
I Can See Clearly Now : Image Restoration via De-Raining—0
Identity-guided Face Generation with Multi-modal Contour Conditions—0
IFR: Iterative Fusion Based Recognizer For Low Quality Scene Text Recognition—0
iiTransformer: A Unified Approach to Exploiting Local and Non-Local Information for Image Restoration—0
Image Deblurring using GAN—0
Image denoising based on improved data-driven sparse representation—0
Image Denoising Via Collaborative Support-Agnostic Recovery—0
Image denoising with generalized Gaussian mixture model patch priors—0
Image Desaturation for SDO/AIA Using Deep Learning—0
Image Inpainting Using Directional Tensor Product Complex Tight Framelets—0
Image Inpainting using Partial Convolution—0
Image Quality Assessment for Perceptual Image Restoration: A New Dataset, Benchmark and Metric—0
Image Restoration: A Comparative Analysis of Image De noising Using Different Spatial Filtering Techniques—0
Image Restoration: A General Wavelet Frame Based Model and Its Asymptotic Analysis—0
Image Restoration and Reconstruction using Variable Splitting and Class-adapted Image Priors—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