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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6251–6275 of 7282 papers

TitleStatusHype
Image Denoising with Graph-Convolutional Neural NetworksCode0
Educating Text Autoencoders: Latent Representation Guidance via DenoisingCode0
Invertible generative models for inverse problems: mitigating representation error and dataset biasCode0
Quantization-Based Regularization for AutoencodersCode0
GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy ImagesCode0
A Research and Strategy of Remote Sensing Image Denoising Algorithms—0
Tractable Approach to MmWaves Cellular Analysis with FSO Backhauling under Feedback Delay and Hardware LimitationsCode0
Image Fusion via Sparse Regularization with Non-Convex PenaltiesCode0
Generative Imaging and Image Processing via Generative Encoder—0
Segmentation-Aware Image Denoising without Knowing True SegmentationCode0
Underwater Color Restoration Using U-Net Denoising Autoencoder—0
Side Window FilteringCode0
Constrained low-tubal-rank tensor recovery for hyperspectral images mixed noise removal by bilateral random projections—0
Plug-and-Play Methods Provably Converge with Properly Trained DenoisersCode0
Joint Demosaicking and Denoising by Fine-Tuning of Bursts of Raw ImagesCode0
Almost Unsupervised Text to Speech and Automatic Speech Recognition—0
Block Coordinate Regularization by DenoisingCode0
Deep Plug-and-play Prior for Low-rank Tensor Completion—0
Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution PipelineCode0
Missing Data Imputation with Adversarially-trained Graph Convolutional NetworksCode0
Learning to Denoise Distantly-Labeled Data for Entity TypingCode0
Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot LearningCode0
LEARNING GENERATIVE MODELS FOR DEMIXING OF STRUCTURED SIGNALS FROM THEIR SUPERPOSITION USING GANS—0
Multi-level Encoder-Decoder Architectures for Image Restoration—0
Deep Denoising: Rate-Optimal Recovery of Structured Signals with a Deep Prior—0
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All datasetsDarmstadt Noise DatasetDIV2KDNDiris

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81—Unverified
2Pixel-shuffling DownsamplingPSNR38.4—Unverified
3TWSCPSNR37.93—Unverified
4CBDNet(Syn)PSNR37.57—Unverified
5MCWNNMPSNR37.38—Unverified
6Han et alPSNR35.95—Unverified
7FFDNetPSNR34.4—Unverified
8TNRDPSNR33.65—Unverified
9CDnCNN-BPSNR32.43—Unverified
10NLRNPSNR30.8—Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64—Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61—Unverified