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

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Showing 501–525 of 1459 papers

TitleStatusHype
Concatenated Attention Neural Network for Image Restoration—0
Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models—0
Enhancing Sample Generation of Diffusion Models using Noise Level Correction—0
Concept for a CMOS Image Sensor Suited for Analog Image Pre-Processing—0
Adaptive Image Restoration for Video Surveillance: A Real-Time Approach—0
Performance of a GPU- and Time-Efficient Pseudo 3D Network for Magnetic Resonance Image Super-Resolution and Motion Artifact Reduction—0
Haar Nuclear Norms with Applications to Remote Sensing Imagery Restoration—0
Enhancing Image Restoration Transformer via Adaptive Translation Equivariance—0
Computationally iterative methods for salt-and-pepper denoising—0
GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity—0
Compressive Shack-Hartmann Wavefront Sensor based on Deep Neural Networks—0
Enhance Visual Recognition under Adverse Conditions via Deep Networks—0
A Survey on Hyperspectral Image Restoration: From the View of Low-Rank Tensor Approximation—0
Enhancement of Underwater Images with Statistical Model of Background Light and Optimization of Transmission Map—0
Compression, Restoration, Re-sampling, Compressive Sensing: Fast Transforms in Digital Imaging—0
All-in-one Weather-degraded Image Restoration via Adaptive Degradation-aware Self-prompting Model—0
Energy-Inspired Self-Supervised Pretraining for Vision Models—0
A Survey on Diffusion Models for Inverse Problems—0
Guided Frequency Loss for Image Restoration—0
Empowering Image Recovery_ A Multi-Attention Approach—0
Empirical Bayesian image restoration by Langevin sampling with a denoising diffusion implicit prior—0
All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation—0
Elucidating and Endowing the Diffusion Training Paradigm for General Image Restoration—0
Efficient Progressive High Dynamic Range Image Restoration via Attention and Alignment Network—0
Complexity Experts are Task-Discriminative Learners for Any Image Restoration—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