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 3501–3525 of 7282 papers

TitleStatusHype
Graph Signal Restoration Using Nested Deep Algorithm Unrolling—0
Deep learning for full-field ultrasonic characterization—0
Graphs as Tools to Improve Deep Learning Methods—0
Multiscale Structure Guided Diffusion for Image Deblurring—0
Deep Learning for 3D Point Cloud Enhancement: A Survey—0
Image Deconvolution with Deep Image and Kernel Priors—0
Image Denoising and Inpainting with Deep Neural Networks—0
Image denoising and restoration with CNN-LSTM Encoder Decoder with Direct Attention—0
Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network—0
A Variational Approach to Shape-from-shading Under Natural Illumination—0
Image denoising as a conditional expectation—0
Image denoising based on improved data-driven sparse representation—0
Image Denoising by Gaussian Patch Mixture Model and Low Rank Patches—0
Image denoising by Super Neurons: Why go deep?—0
Graph Sanitation with Application to Node Classification—0
Image denoising in acoustic field microscopy—0
Image Denoising in FPGA using Generic Risk Estimation—0
Image Denoising Inspired by Quantum Many-Body physics—0
Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --—0
Image denoising through bivariate shrinkage function in framelet domain—0
Graph Representation Learning with Diffusion Generative Models—0
Deep learning denoising for EOG artifacts removal from EEG signals—0
Image Denoising Using Convolutional Autoencoder—0
Graph Neural Networks and Differential Equations: A hybrid approach for data assimilation of fluid flows—0
Deep learning denoiser assisted roughness measurements extraction from thin resists with low Signal-to-Noise Ratio(SNR) SEM images: analysis with SMILE—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