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 21012125 of 7282 papers

TitleStatusHype
Automated characterization of noise distributions in diffusion MRI dataCode0
Language-Aware Multilingual Machine Translation with Self-Supervised LearningCode0
Language-Guided Diffusion Model for Visual GroundingCode0
Resurrecting Label Propagation for Graphs with Heterophily and Label NoiseCode0
Labeling, Cutting, Grouping: an Efficient Text Line Segmentation Method for Medieval ManuscriptsCode0
Language Model Preference Evaluation with Multiple Weak EvaluatorsCode0
k-Sparse AutoencodersCode0
AutoJoin: Efficient Adversarial Training against Gradient-Free Perturbations for Robust Maneuvering via Denoising Autoencoder and Joint LearningCode0
Kronecker-structured Sparse Vector Recovery with Application to IRS-MIMO Channel EstimationCode0
Deep Decoder: Concise Image Representations from Untrained Non-convolutional NetworksCode0
Deep CSI Learning for Gait Biometric Sensing and RecognitionCode0
Knowledge Enhanced Multi-intent Transformer Network for RecommendationCode0
Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual NetworkCode0
Autoencoders, Kernels, and Multilayer Perceptrons for Electron Micrograph Restoration and CompressionCode0
Deep Class Aware DenoisingCode0
Massive Styles Transfer with Limited Labeled DataCode0
KADEL: Knowledge-Aware Denoising Learning for Commit Message GenerationCode0
Deep Burst DenoisingCode0
Autoencoder-based prediction of ICU clinical codesCode0
Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source SeparationCode0
Joint Visual Denoising and Classification using Deep LearningCode0
Joint inference and input optimization in equilibrium networksCode0
DeepASL: Kinetic Model Incorporated Loss for Denoising Arterial Spin Labeled MRI via Deep Residual LearningCode0
A Unified View on Graph Neural Networks as Graph Signal DenoisingCode0
A Brief Review of Real-World Color Image DenoisingCode0
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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