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

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Showing 6251–6275 of 7282 papers

TitleStatusHype
Finding Local Diffusion Schrödinger Bridge using Kolmogorov-Arnold NetworkCode0
Finding Local Diffusion Schrodinger Bridge using Kolmogorov-Arnold NetworkCode0
Fine-grained Contrastive Learning for Relation ExtractionCode0
Fine-grained Forecasting Models Via Gaussian Process Blurring EffectCode0
CONCORD: Concept-Informed Diffusion for Dataset DistillationCode0
Recursive nearest agglomeration (ReNA): fast clustering for approximation of structured signalsCode0
Regularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited DataCode0
Perception-based multiplicative noise removal using SDEsCode0
Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion modelsCode0
DCANet: Dual Convolutional Neural Network with Attention for Image Blind DenoisingCode0
RED-PSM: Regularization by Denoising of Factorized Low Rank Models for Dynamic ImagingCode0
MRI Recovery with Self-Calibrated Denoisers without Fully-Sampled DataCode0
Fingerprint Presentation Attack Detection by Channel-wise Feature DenoisingCode0
Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video DenoisingCode0
Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic ModelCode0
Denoising Distantly Supervised Open-Domain Question AnsweringCode0
Are We Using Autoencoders in a Wrong Way?Code0
TP-NoDe: Topology-aware Progressive Noising and Denoising of Point Clouds towards UpsamplingCode0
FiRe: Fixed-points of Restoration Priors for Solving Inverse ProblemsCode0
First image then video: A two-stage network for spatiotemporal video denoisingCode0
First line of defense: A robust first layer mitigates adversarial attacksCode0
MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient NetworkCode0
MSSIDD: A Benchmark for Multi-Sensor DenoisingCode0
Joint Demosaicking and Denoising by Fine-Tuning of Bursts of Raw ImagesCode0
Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial DefenseCode0
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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