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 6126–6150 of 7282 papers

TitleStatusHype
Deep priors for satellite image restoration with accurate uncertainties—0
Speaking in Wavelet Domain: A Simple and Efficient Approach to Speed up Speech Diffusion Model—0
SpecGrad: Diffusion Probabilistic Model based Neural Vocoder with Adaptive Noise Spectral Shaping—0
Speckle Noise Reduction in Medical Ultrasound Images—0
Deep Reward Supervisions for Tuning Text-to-Image Diffusion Models—0
Speckle Noise Reduction in Ultrasound Images using Denoising Auto-encoder with Skip Connection—0
DeepSpace: Dynamic Spatial and Source Cue Based Source Separation for Dialog Enhancement—0
Deep Sparse Coding Using Optimized Linear Expansion of Thresholds—0
Deep Spectral Prior—0
Deep Speech Denoising with Vector Space Projections—0
Speckles-Training-Based Denoising Convolutional Neural Network Ghost Imaging—0
G2P-DDM: Generating Sign Pose Sequence from Gloss Sequence with Discrete Diffusion Model—0
Deep Stacked Networks with Residual Polishing for Image Inpainting—0
DeepTensor: Low-Rank Tensor Decomposition with Deep Network Priors—0
Deep Transfer Tensor Factorization for Multi-View Learning—0
Deep Transform and Metric Learning Network: Wedding Deep Dictionary Learning and Neural Networks—0
Deep Ultrasound Denoising Using Diffusion Probabilistic Models—0
Deep Ultrasound Denoising Without Clean Data—0
Deep Unfolding-Aided Parameter Tuning for Plug-and-Play-Based Video Snapshot Compressive Imaging—0
Deep Unfolding with Normalizing Flow Priors for Inverse Problems—0
Spectral Analysis of Representational Similarity with Limited Neurons—0
Deep unrolling for learning optimal spatially varying regularisation parameters for Total Generalised Variation—0
Deep unsupervised 3D human body reconstruction from a sparse set of landmarks—0
Deep Variation Prior: Joint Image Denoising and Noise Variance Estimation without Clean Data—0
Defending Against Adversarial Iris Examples Using Wavelet Decomposition—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