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 5376–5400 of 7282 papers

TitleStatusHype
A Survey on Patch-based Synthesis: GPU Implementation and Optimization—0
A Survey on the Visual Perceptions of Gaussian Noise Filtering on Photography—0
Asymmetric Diffusion Based Channel-Adaptive Secure Wireless Semantic Communications—0
Variance reduction of diffusion model's gradients with Taylor approximation-based control variate—0
Asymptotic Analysis of LASSOs Solution Path with Implications for Approximate Message Passing—0
AsymRnR: Video Diffusion Transformers Acceleration with Asymmetric Reduction and Restoration—0
Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors—0
Simultaneous Denoising and Dereverberation Using Deep Embedding Features—0
A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets—0
Simultaneous Denoising and Localization Network for Photoacoustic Target Localization—0
A task-specific deep-learning-based denoising approach for myocardial perfusion SPECT—0
Simultaneous Denoising and Motion Estimation for Low-dose Gated PET using a Siamese Adversarial Network with Gate-to-Gate Consistency Learning—0
A Theoretical Justification for Image Inpainting using Denoising Diffusion Probabilistic Models—0
A Tiered Move-making Algorithm for General Non-submodular Pairwise Energies—0
A Time-Series Data Augmentation Model through Diffusion and Transformer Integration—0
A Time-Vertex Signal Processing Framework—0
A Topological Loss Function: Image Denoising on a Low-Light Dataset—0
A Total Variation Denoising Method Based on Median Filter and Phase Consistency—0
A Training-Free Plug-and-Play Watermark Framework for Stable Diffusion—0
A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising—0
A Truncated EM Approach for Spike-and-Slab Sparse Coding—0
Attacks and Defenses for Generative Diffusion Models: A Comprehensive Survey—0
Attentional Graph Neural Network Is All You Need for Robust Massive Network Localization—0
Attention-based network for low-light image enhancement—0
Attention-based Neural Cellular Automata—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