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 2401–2425 of 7282 papers

TitleStatusHype
Zeroth-Order Sampling Methods for Non-Log-Concave Distributions: Alleviating Metastability by Denoising DiffusionCode0
Denoising Diffusion Models for Inpainting of Healthy Brain Tissue—0
Label-Noise Robust Diffusion ModelsCode1
Purified and Unified Steganographic NetworkCode1
RISAR: RIS-assisted Human Activity Recognition with Commercial Wi-Fi Devices—0
Seeing and Hearing: Open-domain Visual-Audio Generation with Diffusion Latent Aligners—0
Diffusion Model-Based Image Editing: A SurveyCode4
Structure-Guided Adversarial Training of Diffusion Models—0
GEM3D: GEnerative Medial Abstractions for 3D Shape Synthesis—0
Multi-LoRA Composition for Image Generation—0
Generative AI in Vision: A Survey on Models, Metrics and Applications—0
Photon-counting CT using a Conditional Diffusion Model for Super-resolution and Texture-preservation—0
Diffusion Posterior Proximal Sampling for Image RestorationCode1
HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion ModelsCode2
IRConStyle: Image Restoration Framework Using Contrastive Learning and Style TransferCode0
Frustratingly Simple Prompting-based Text Denoising—0
Pretraining Strategy for Neural PotentialsCode0
A Study of Shape Modeling Against Noise—0
Label-efficient Multi-organ Segmentation Method with Diffusion Model—0
Let's Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion ModelsCode1
Background Denoising for Ptychography via Wigner Distribution Deconvolution—0
Seamless Human Motion Composition with Blended Positional EncodingsCode3
On normalization-equivariance properties of supervised and unsupervised denoising methods: a survey—0
GeneOH Diffusion: Towards Generalizable Hand-Object Interaction Denoising via Denoising DiffusionCode2
PeriodGrad: Towards Pitch-Controllable Neural Vocoder Based on a Diffusion Probabilistic Model—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