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 16761700 of 7282 papers

TitleStatusHype
A Deep Learning Method for Simultaneous Denoising and Missing Wedge Reconstruction in Cryogenic Electron TomographyCode1
Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales?Code1
Deep Plug-and-Play Prior for Hyperspectral Image RestorationCode1
DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain PackingCode1
Self2Self+: Single-Image Denoising with Self-Supervised Learning and Image Quality Assessment LossCode1
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and ComprehensionCode1
DiffPO: A causal diffusion model for learning distributions of potential outcomesCode1
Deep Random Projector: Accelerated Deep Image PriorCode1
Deep Recurrent Neural Networks for ECG Signal DenoisingCode1
Diff-Reg v1: Diffusion Matching Model for Registration ProblemCode1
Deep Reparametrization of Multi-Frame Super-Resolution and DenoisingCode1
Diffusion Model for Dense MatchingCode1
Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User CommunicationsCode1
Deep Residual Network Empowered Channel Estimation for IRS-Assisted Multi-User Communication SystemsCode1
Self-supervised Low Light Image Enhancement and DenoisingCode1
DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic ModelsCode1
The Lottery Ticket Hypothesis in Denoising: Towards Semantic-Driven InitializationCode1
Deep Semantic Statistics Matching (D2SM) Denoising NetworkCode1
Semi-Implicit Denoising Diffusion Models (SIDDMs)Code1
Empowering Diffusion Models on the Embedding Space for Text GenerationCode1
Designing and Training of A Dual CNN for Image DenoisingCode1
Ship in Sight: Diffusion Models for Ship-Image Super ResolutionCode1
Show, Edit and Tell: A Framework for Editing Image CaptionsCode1
Probabilistic Noise2Void: Unsupervised Content-Aware DenoisingCode1
CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion ModelsCode1
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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