SOTAVerified

Denoising

Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.

( Image credit: Beyond a Gaussian Denoiser )

Papers

Showing 20012025 of 7282 papers

TitleStatusHype
Learning Robust 3D Representation from CLIP via Dual DenoisingCode0
Deep Retinex Decomposition for Low-Light EnhancementCode0
Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging ProblemsCode0
Deep Residual Autoencoders for Expectation Maximization-inspired Dictionary LearningCode0
Adaptive Mixing of Auxiliary Losses in Supervised LearningCode0
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving AugmentationCode0
DeepRED: Deep Image Prior Powered by REDCode0
All You Need is RAW: Defending Against Adversarial Attacks with Camera Image PipelinesCode0
Learning Pixel-Distribution Prior with Wider Convolution for Image DenoisingCode0
Learning normalized image densities via dual score matchingCode0
Learning of Patch-Based Smooth-Plus-Sparse Models for Image ReconstructionCode0
Accelerated Gossip in Networks of Given Dimension using Jacobi Polynomial IterationsCode0
Learning parametric dictionaries for graph signalsCode0
Learning Priors in High-frequency Domain for Inverse Imaging ReconstructionCode0
Learning in Deep Factor Graphs with Gaussian Belief PropagationCode0
Deep Perceptual Enhancement for Medical Image AnalysisCode0
Deep Perceptual Enhancement for Medical Image AnalysisCode0
Balancing User Preferences by Social Networks: A Condition-Guided Social Recommendation Model for Mitigating Popularity BiasCode0
Learning Instance-Specific Parameters of Black-Box Models Using Differentiable SurrogatesCode0
Balancing the Style-Content Trade-Off in Sentiment Transfer Using Polarity-Aware DenoisingCode0
Deep Pairwise Hashing for Cold-start RecommendationCode0
Differentiable Surface Splatting for Point-based Geometry ProcessingCode0
DeepOrientation: convolutional neural network for fringe pattern orientation map estimationCode0
Noisy Batch Active Learning with Deterministic AnnealingCode0
Learning Joint Denoising, Demosaicing, and Compression from the Raw Natural Image Noise DatasetCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified