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 4226–4250 of 7282 papers

TitleStatusHype
HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing—0
A Dual Sparse Decomposition Method for Image Denoising—0
A Dual-Purpose Framework for Backdoor Defense and Backdoor Amplification in Diffusion Models—0
A Dual-Module Denoising Approach with Curriculum Learning for Enhancing Multimodal Aspect-Based Sentiment Analysis—0
Human Video Translation via Query Warping—0
Hume: Introducing System-2 Thinking in Visual-Language-Action Model—0
HunyuanPortrait: Implicit Condition Control for Enhanced Portrait Animation—0
Tencent Text-Video Retrieval: Hierarchical Cross-Modal Interactions with Multi-Level Representations—0
HW-TSC’s Participation in the WMT 2021 Triangular MT Shared Task—0
Hybrid diffusion models: combining supervised and generative pretraining for label-efficient fine-tuning of segmentation models—0
Hybrid Digital-Analog Semantic Communications—0
Hybrid Driven Learning for Channel Estimation in Intelligent Reflecting Surface Aided Millimeter Wave Communications—0
HybridoNet-Adapt: A Domain-Adapted Framework for Accurate Lithium-Ion Battery RUL Prediction—0
Text-Audio-Visual-conditioned Diffusion Model for Video Saliency Prediction—0
Hybrid Training of Denoising Networks to Improve the Texture Acutance of Digital Cameras—0
HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model—0
Text-Aware Image Restoration with Diffusion Models—0
Hypergraph Spectral Analysis and Processing in 3D Point Cloud—0
Hyper-Laplacian Regularized Unidirectional Low-Rank Tensor Recovery for Multispectral Image Denoising—0
Hyper-MD: Mesh Denoising with Customized Parameters Aware of Noise Intensity and Geometric Characteristics—0
Hyper-Restormer: A General Hyperspectral Image Restoration Transformer for Remote Sensing Imaging—0
TextDestroyer: A Training- and Annotation-Free Diffusion Method for Destroying Anomal Text from Images—0
Hyperspectral Denoising Using Unsupervised Disentangled Spatio-Spectral Deep Priors—0
Text-Driven Fashion Image Editing with Compositional Concept Learning and Counterfactual Abduction—0
Hyperspectral Image Denoising Based On Multi-Stream Denoising Network—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