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 6576–6600 of 7282 papers

TitleStatusHype
Joint Bilateral Filter for Signal Recovery from Phase Preserved Curvelet Coefficients for Image Denoising—0
Deep Neural Networks Motivated by Partial Differential EquationsCode0
Simultaneous Fidelity and Regularization Learning for Image RestorationCode0
X-ray ghost tomography: denoising, dose fractionation and mask considerations—0
DeepASL: Kinetic Model Incorporated Loss for Denoising Arterial Spin Labeled MRI via Deep Residual LearningCode0
Real-world Noisy Image Denoising: A New BenchmarkCode0
Adaptive Quantile Sparse Image (AQuaSI) Prior for Inverse Imaging ProblemsCode0
Learning to Separate Object Sounds by Watching Unlabeled VideoCode0
Regional Priority Based Anomaly Detection using Autoencoders—0
Efficient First-Order Algorithms for Adaptive Signal DenoisingCode0
Context-aware Deep Feature Compression for High-speed Visual TrackingCode0
Stochastic Variational Inference with Gradient Linearization—0
Multi-scale Processing of Noisy Images using Edge Preservation LossesCode0
SUNLayer: Stable denoising with generative networks—0
Lifting Layers: Analysis and ApplicationsCode0
Group Sparsity Residual with Non-Local Samples for Image Denoising—0
3D Point Cloud Denoising using Graph Laplacian Regularization of a Low Dimensional Manifold Model—0
Discrete Potts Model for Generating Superpixels on Noisy Images—0
Example-based super-resolution for point-cloud video—0
Semantic Adversarial ExamplesCode0
Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising NetworksCode0
Correction by Projection: Denoising Images with Generative Adversarial Networks—0
Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping—0
Cross-domain Recommendation via Deep Domain Adaptation—0
Learning Filter Bank Sparsifying Transforms—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