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 3126–3150 of 7282 papers

TitleStatusHype
Denoising Diffusion Probabilistic Models for Image Inpainting of Cell Distributions in the Human Brain—0
BioAtt: Anatomical Prior Driven Low-Dose CT Denoising—0
A multistep segmentation algorithm for vessel extraction in medical imaging—0
Adaptive Spatial-Spectral Dictionary Learning for Hyperspectral Image Denoising—0
3DDesigner: Towards Photorealistic 3D Object Generation and Editing with Text-guided Diffusion Models—0
Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communications—0
Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild—0
Denoising diffusion probabilistic models for probabilistic energy forecasting—0
Binary Diffusion Probabilistic Model—0
A Multi-Stage Triple-Path Method for Speech Separation in Noisy and Reverberant Environments—0
Denoising Diffusion Probabilistic Models for Styled Walking Synthesis—0
Denoising diffusion probabilistic models are optimally adaptive to unknown low dimensionality—0
Bilingual-GAN: A Step Towards Parallel Text Generation—0
A Multi-scale Video Denoising Algorithm for Raw Image—0
Adaptive Segmentation-Based Initialization for Steered Mixture of Experts Image Regression—0
Bilevel learning of l1-regularizers with closed-form gradients(BLORC)—0
A Multiscale Image Denoising Algorithm Based On Dilated Residual Convolution Network—0
Denoising Diffusion Models for Inpainting of Healthy Brain Tissue—0
Denoising diffusion models for high-resolution microscopy image restoration—0
Denoising Diffusion Models for Anomaly Localization in Medical Images—0
Bilateral Spectrum Weighted Total Variation for Noisy-Image Super-Resolution and Image Denoising—0
A Multiscale Gradient Fusion Method for Edge Detection in Color Images Utilizing the CBM3D Filter—0
Adaptive Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising and Destriping—0
Denoising Diffusion Medical Models—0
Bilateral Denoising Diffusion Models—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