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 1801–1825 of 7282 papers

TitleStatusHype
Bootstrap Diffusion Model Curve Estimation for High Resolution Low-Light Image Enhancement—0
Bootstrap3D: Improving Multi-view Diffusion Model with Synthetic Data—0
Anatomically and Metabolically Informed Diffusion for Unified Denoising and Segmentation in Low-Count PET Imaging—0
BOOT: Data-free Distillation of Denoising Diffusion Models with Bootstrapping—0
Boosting with Structural Sparsity: A Differential Inclusion Approach—0
Anatomical-Guided Attention Enhances Unsupervised PET Image Denoising Performance—0
Boosting the Performance of Plug-and-Play Priors via Denoiser Scaling—0
An Artificial Intelligence Enabled Signature Estimation of Dual Wideband Systems in Ultra-Low Signal-to-Noise Ratio—0
Add and Thin: Diffusion for Temporal Point Processes—0
Boosting of Image Denoising Algorithms—0
Boosting Image Restoration via Priors from Pre-trained Models—0
An approach to image denoising using manifold approximation without clean images—0
Boosting Fast and High-Quality Speech Synthesis with Linear Diffusion—0
BOOSTING ENCODER-DECODER CNN FOR INVERSE PROBLEMS—0
An Approach for Reducing Outliers of Non Local Means Image Denoising Filter—0
A Data-Driven Paradigm-Based Image Denoising and Mosaicking Approach for High-Resolution Acoustic Camera—0
Certified Robustness to Clean-Label Poisoning Using Diffusion Denoising—0
Diffusion-Driven Semantic Communication for Generative Models with Bandwidth Constraints—0
Boosting Diffusion Models with Moving Average Sampling in Frequency Domain—0
A data-driven modular architecture with denoising autoencoders for health indicator construction in a manufacturing process—0
Accelerating Diffusion Sampling via Exploiting Local Transition Coherence—0
An Analysis on Quantizing Diffusion Transformers—0
BM3D vs 2-Layer ONN—0
Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity—0
Diffusion-Based Symbolic Regression—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