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 6701–6725 of 7282 papers

TitleStatusHype
Calibration of depth cameras using denoised depth images—0
On the exact relationship between the denoising function and the data distribution—0
Label Denoising Adversarial Network (LDAN) for Inverse Lighting of Face Images—0
An inner-loop free solution to inverse problems using deep neural networks—0
Weighted Low-rank Tensor Recovery for Hyperspectral Image Restoration—0
Convolutional Sparse Coding with Overlapping Group Norms—0
On denoising autoencoders trained to minimise binary cross-entropy—0
Sharpness-aware Low dose CT denoising using conditional generative adversarial networkCode0
VIGAN: Missing View Imputation with Generative Adversarial NetworksCode0
Dilated Deep Residual Network for Image Denoising—0
Knock-Knock: Acoustic Object Recognition by using Stacked Denoising Autoencoders—0
An ELU Network with Total Variation for Image Denoising—0
Collaborative Filtering using Denoising Auto-Encoders for Market Basket Data—0
Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization—0
Anomaly Detection with Robust Deep AutoencodersCode0
Interpretable Low-Dimensional Regression via Data-Adaptive Smoothing—0
Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual LossCode1
An End-to-End Compression Framework Based on Convolutional Neural NetworksCode0
Dictionary Learning Based on Sparse Distribution Tomography—0
From Patches to Images: A Nonparametric Generative ModelCode0
Image Denoising via CNNs: An Adversarial Approach—0
Transfer Learning with Label Noise—0
Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual NetworkCode0
Learning Pixel-Distribution Prior with Wider Convolution for Image DenoisingCode0
Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis—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