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 951–1000 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
Image Restoration by Combined Order Regularization with Optimal Spatial Adaptation—0
Image Restoration by Deep Projected GSURE—0
Image Restoration by Estimating Frequency Distribution of Local Patches—0
Progressive Image Super-Resolution via Neural Differential Equation—0
Image Restoration for Remote Sensing: Overview and Toolbox—0
Image Restoration for Under-Display Camera—0
Image Restoration from Parametric Transformations using Generative Models—0
Image Restoration from Patch-based Compressed Sensing Measurement—0
Image Restoration in Non-Linear Filtering Domain using MDB approach—0
Image Restoration Learning via Noisy Supervision in the Fourier Domain—0
Image Restoration using Autoencoding Priors—0
Image Restoration Using Conditional Random Fields and Scale Mixtures of Gaussians—0
Image Restoration using Feature-guidance—0
Image Restoration Using Joint Statistical Modeling in Space-Transform Domain—0
Image restoration using sparse approximations of spatially varying blur operators in the wavelet domain—0
Image Restoration using Total Variation Regularized Deep Image Prior—0
Image Restoration using Total Variation with Overlapping Group Sparsity—0
Image Restoration with Locally Selected Class-Adapted Models—0
Image Restoration with Point Spread Function Regularization and Active Learning—0
Image Super-Resolution with Guarantees via Conformalized Generative Models—0
Improving Bracket Image Restoration and Enhancement with Flow-guided Alignment and Enhanced Feature Aggregation—0
Improving Cross-modal Alignment for Text-Guided Image Inpainting—0
Improving Image Restoration with Soft-Rounding—0
Improving Feature Stability during Upsampling -- Spectral Artifacts and the Importance of Spatial Context—0
Impulsive noise removal from color images with morphological filtering—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