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 2801–2825 of 7282 papers

TitleStatusHype
Factorized Diffusion Architectures for Unsupervised Image Generation and Segmentation—0
Factorized Diffusion: Perceptual Illusions by Noise Decomposition—0
Fair Primal Dual Splitting Method for Image Inverse Problems—0
FairQueue: Rethinking Prompt Learning for Fair Text-to-Image Generation—0
Cross-domain Recommendation via Deep Domain Adaptation—0
Functions with average smoothness: structure, algorithms, and learning—0
FaR: Enhancing Multi-Concept Text-to-Image Diffusion via Concept Fusion and Localized Refinement—0
Far-Field Speaker Recognition Benchmark Derived From The DiPCo Corpus—0
Crossfusor: A Cross-Attention Transformer Enhanced Conditional Diffusion Model for Car-Following Trajectory Prediction—0
Fast 3D Point Cloud Denoising via Bipartite Graph Approximation & Total Variation—0
Exploiting network optimization stability for enhanced PET image denoising using deep image prior—0
Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learning—0
Correlation Distance Skip Connection Denoising Autoencoder (CDSK-DAE) for Speech Feature Enhancement—0
Fast and Accurate Poisson Denoising with Optimized Nonlinear Diffusion—0
Exploiting Multilingualism in Low-resource Neural Machine Translation via Adversarial Learning—0
Exploiting Model Sparsity in Adaptive MPC: A Compressed Sensing Viewpoint—0
Fast and High-Quality Image Denoising via Malleable Convolutions—0
CrossMatch: Source-Free Domain Adaptive Semantic Segmentation via Cross-Modal Consistency Training—0
Fast and Light-Weight Network for Single Frame Structured Illumination Microscopy Super-Resolution—0
Fast and Accurate Estimation of Low-Rank Matrices from Noisy Measurements via Preconditioned Non-Convex Gradient Descent—0
Fast and Provable ADMM for Learning with Generative Priors—0
Correction by Projection: Denoising Images with Generative Adversarial Networks—0
A Simple Analysis of Discretization Error in Diffusion Models—0
Fast and Robust Visuomotor Riemannian Flow Matching Policy—0
Explicit Diffusion of Gaussian Mixture Model Based Image Priors—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