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 4151–4175 of 7282 papers

TitleStatusHype
SRNR: Training neural networks for Super-Resolution MRI using Noisy high-resolution Reference data—0
Lightweight network towards real-time image denoising on mobile devicesCode0
Spiking sampling network for image sparse representation and dynamic vision sensor data compression—0
DPCSpell: A Transformer-based Detector-Purificator-Corrector Framework for Spelling Error Correction of Bangla and Resource Scarce Indic LanguagesCode1
From Denoising Diffusions to Denoising Markov ModelsCode1
Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image GenerationCode2
Few-shot Image Generation with Diffusion ModelsCode0
WeakIdent: Weak formulation for Identifying Differential Equations using Narrow-fit and TrimmingCode0
Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery—0
A Simple and Robust Correlation Filtering Method for Text-based Person SearchCode1
Self-Supervised Learning for Speech Enhancement through SynthesisCode0
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion—0
Self Supervised Low Dose Computed Tomography Image Denoising Using Invertible Network Exploiting Inter Slice Congruence—0
Galaxy Image Deconvolution for Weak Gravitational Lensing with Unrolled Plug-and-Play ADMMCode1
Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation—0
Alternating Phase Langevin Sampling with Implicit Denoiser Priors for Phase Retrieval—0
A new method for determining Wasserstein 1 optimal transport maps from Kantorovich potentials, with deep learning applicationsCode0
On the Benefit of Dual-domain Denoising in a Self-supervised Low-dose CT SettingCode1
Unsupervised denoising for sparse multi-spectral computed tomography—0
Attention-based Neural Cellular Automata—0
Self-supervised Physics-based Denoising for Computed Tomography—0
DensePure: Understanding Diffusion Models towards Adversarial Robustness—0
Denoising neural networks for magnetic resonance spectroscopy—0
Intelligent Painter: Picture Composition With Resampling Diffusion ModelCode1
DiffusER: Discrete Diffusion via Edit-based Reconstruction—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