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 2476–2500 of 7282 papers

TitleStatusHype
Hyperspectral Image Denoising via Spatial-Spectral Recurrent TransformerCode0
Hyperspectral Image Denoising via Self-Modulating Convolutional Neural NetworksCode0
Hyperspectral Image Denoising and Anomaly Detection Based on Low-rank and Sparse RepresentationsCode0
Hyperparameter selection for Discrete Mumford-ShahCode0
Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural NetworkCode0
Double Correction Framework for Denoising RecommendationCode0
DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET ImagingCode0
HyperAid: Denoising in hyperbolic spaces for tree-fitting and hierarchical clusteringCode0
IBO: Inpainting-Based Occlusion to Enhance Explainable Artificial Intelligence Evaluation in HistopathologyCode0
Improved Diffusion-based Generative Model with Better Adversarial RobustnessCode0
How to Segment in 3D Using 2D Models: Automated 3D Segmentation of Prostate Cancer Metastatic Lesions on PET Volumes Using Multi-angle Maximum Intensity Projections and Diffusion ModelsCode0
Invertible generative models for inverse problems: mitigating representation error and dataset biasCode0
How Does Diffusion Influence Pretrained Language Models on Out-of-Distribution Data?Code0
Domain Transfer in Latent Space (DTLS) Wins on Image Super-Resolution -- a Non-Denoising ModelCode0
HPPP: Halpern-type Preconditioned Proximal Point Algorithms and Applications to Image RestorationCode0
DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited DataCode0
Holistic Guidance for Occluded Person Re-IdentificationCode0
Domain-Adversarial Neural NetworksCode0
Combining Denoising Autoencoders with Contrastive Learning to fine-tune Transformer ModelsCode0
Combining a Context Aware Neural Network with a Denoising Autoencoder for Measuring String SimilaritiesCode0
How Control Information Influences Multilingual Text Image Generation and Editing?Code0
Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient NetworkCode0
An Underparametrized Deep Decoder Architecture for Graph SignalsCode0
Combatting Adversarial Attacks through Denoising and Dimensionality Reduction: A Cascaded Autoencoder ApproachCode0
RDSA: A Robust Deep Graph Clustering Framework via Dual Soft AssignmentCode0
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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