SOTAVerified

Image Denoising

Image Denoising is a computer vision task that involves removing noise from an image. Noise can be introduced into an image during acquisition or processing, and can reduce image quality and make it difficult to interpret. Image denoising techniques aim to restore an image to its original quality by reducing or removing the noise, while preserving the important features of the image.

( Image credit: Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior )

Papers

Showing 110 of 1220 papers

TitleStatusHype
Energy-Based Transformers are Scalable Learners and ThinkersCode4
Learning Multi-scale Spatial-frequency Features for Image Denoising0
A Real-time Endoscopic Image Denoising System0
Revisiting Transformers with Insights from Image Filtering0
Pseudo-Siamese Blind-Spot Transformers for Self-Supervised Real-World DenoisingCode0
YOND: Practical Blind Raw Image Denoising Free from Camera-Specific Data Dependency0
A Poisson-Guided Decomposition Network for Extreme Low-Light Image Enhancement0
Unrolling Nonconvex Graph Total Variation for Image Denoising0
Optimal Weighted Convolution for Classification and DenosingCode2
Optimal Density Functions for Weighted Convolution in Learning ModelsCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AKDTAverage PSNR35.64Unverified
2MaIR+PSNR35.42Unverified
3MaIRPSNR35.35Unverified
4SCUNet SCUNetAverage PSNR35.18Unverified