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 6601–6625 of 7282 papers

TitleStatusHype
Learning Deep Convolutional Networks for Demosaicing—0
Slice Sampling Particle Belief Propagation—0
Relational Autoencoder for Feature ExtractionCode0
Weakly-supervised Dictionary Learning—0
Image denoising with generalized Gaussian mixture model patch priors—0
Analysis of Fast Alternating Minimization for Structured Dictionary Learning—0
Denoising Signals in Cognitive Radio Systems Using An Evolutionary Algorithm Based Adaptive Filter—0
Denoising Arterial Spin Labeling Cerebral Blood Flow Images Using Deep Learning—0
Denoising Prior Driven Deep Neural Network for Image RestorationCode0
DeepISP: Towards Learning an End-to-End Image Processing PipelineCode0
A Second Order Cumulant Spectrum Test That a Stochastic Process is Strictly Stationary and a Step Toward a Test for Graph Signal Strict Stationarity—0
Robust Kronecker Component Analysis—0
Image denoising and restoration with CNN-LSTM Encoder Decoder with Direct Attention—0
Deep Network for Simultaneous Decomposition and Classification in UWB-SAR Imagery—0
How should a fixed budget of dwell time be spent in scanning electron microscopy to optimize image quality?—0
Simultaneous Tensor Completion and Denoising by Noise Inequality Constrained Convex Optimization—0
Instance Map based Image Synthesis with a Denoising Generative Adversarial Network—0
Denoising Dictionary Learning Against Adversarial Perturbations—0
Design Exploration of Hybrid CMOS-OxRAM Deep Generative Architectures—0
Learning Implicit Brain MRI Manifolds with Deep Learning—0
Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data—0
Scene-Adapted Plug-and-Play Algorithm with Guaranteed Convergence: Applications to Data Fusion in Imaging—0
Image denoising through bivariate shrinkage function in framelet domain—0
Deep Stacked Networks with Residual Polishing for Image Inpainting—0
Dendritic error backpropagation in deep cortical microcircuitsCode0
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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