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 21512175 of 7282 papers

TitleStatusHype
Labeling, Cutting, Grouping: an Efficient Text Line Segmentation Method for Medieval ManuscriptsCode0
Unmasking Bias in Diffusion Model TrainingCode0
Debiasing Cardiac Imaging with Controlled Latent Diffusion ModelsCode0
Debiased Recommendation with Noisy FeedbackCode0
Adaptive 3D descattering with a dynamic synthesis networkCode0
KADEL: Knowledge-Aware Denoising Learning for Commit Message GenerationCode0
A Two-stage Deep Network for High Dynamic Range Image ReconstructionCode0
Joint Visual Denoising and Classification using Deep LearningCode0
Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source SeparationCode0
Joint inference and input optimization in equilibrium networksCode0
DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNsCode0
Joint Enhancement and Denoising Method via Sequential DecompositionCode0
Attenuation of Seismic Random Noise With Unknown Distribution: A Gaussianization FrameworkCode0
Iterative Joint Image Demosaicking and Denoising using a Residual Denoising NetworkCode0
Attention Based Real Image RestorationCode0
Iterative Residual CNNs for Burst Photography ApplicationsCode0
Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video DenoisingCode0
Iterative Learning for Joint Image Denoising and Motion Artifact Correction of 3D Brain MRICode0
Attention Mechanism Enhanced Kernel Prediction Networks for Denoising of Burst ImagesCode0
Iterative PET Image Reconstruction Using Convolutional Neural Network RepresentationCode0
Attention in a family of Boltzmann machines emerging from modern Hopfield networksCode0
Joint Demosaicking and Denoising by Fine-Tuning of Bursts of Raw ImagesCode0
Joint Multi-Scale Tone Mapping and Denoising for HDR Image EnhancementCode0
Resurrecting Label Propagation for Graphs with Heterophily and Label NoiseCode0
Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation DatasetCode0
Show:102550
← PrevPage 87 of 292Next →

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