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 4551–4575 of 7282 papers

TitleStatusHype
Speech Enhancement with Multi-granularity Vector Quantization—0
Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild—0
Filtered Iterative Denoising for Linear Inverse Problems—0
Score-based Diffusion Models in Function Space—0
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution—0
Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action—0
Deep Transfer Tensor Factorization for Multi-View Learning—0
Metaphor Detection with Effective Context DenoisingCode0
A deep convolutional neural network for salt-and-pepper noise removal using selective convolutional blocksCode0
Language-Aware Multilingual Machine Translation with Self-Supervised LearningCode0
QS-ADN: Quasi-Supervised Artifact Disentanglement Network for Low-Dose CT Image Denoising by Local Similarity Among Unpaired DataCode0
MedDiff: Generating Electronic Health Records using Accelerated Denoising Diffusion Model—0
Coherence and Diversity through Noise: Self-Supervised Paraphrase Generation via Structure-Aware Denoising—0
Noise-cleaning the precision matrix of fMRI time series—0
ShiftDDPMs: Exploring Conditional Diffusion Models by Shifting Diffusion Trajectories—0
Using Intermediate Forward Iterates for Intermediate Generator Optimization—0
Diffusion Model for Generative Image Denoising—0
Offloading Deep Learning Powered Vision Tasks from UAV to 5G Edge Server with Denoising—0
Scalable Lossless Coding of Dynamic Medical CT Data Using Motion Compensated Wavelet Lifting with Denoised Prediction and Update—0
Learning the Night Sky with Deep Generative Priors—0
Compression of Dynamic Medical CT Data Using Motion Compensated Wavelet Lifting with Denoised Update—0
A Theoretical Justification for Image Inpainting using Denoising Diffusion Probabilistic Models—0
Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial DefenseCode0
Energy-Inspired Self-Supervised Pretraining for Vision Models—0
Optimizing Rate-Distortion Performance of Motion Compensated Wavelet Lifting with Denoised Prediction and Update—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