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 6926–6950 of 7282 papers

TitleStatusHype
SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder—0
A two-stage learning method for protein-protein interaction prediction—0
Machine Learning Techniques and Applications For Ground-based Image Analysis—0
Simultaneous Inpainting and Denoising by Directional Global Three-part Decomposition: Connecting Variational and Fourier Domain Based Image Processing—0
Convolution by Evolution: Differentiable Pattern Producing Networks—0
A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets—0
Multi-pretrained Deep Neural Network—0
Multi-View Treelet Transform—0
Multispectral Images Denoising by Intrinsic Tensor Sparsity Regularization—0
Needle-Match: Reliable Patch Matching Under High Uncertainty—0
Minimizing the Maximal Rank—0
From Noise Modeling to Blind Image Denoising—0
A Holistic Approach to Cross-Channel Image Noise Modeling and Its Application to Image Denoising—0
Training Auto-encoders Effectively via Eliminating Task-irrelevant Input Variables—0
Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers—0
Neural Universal Discrete Denoiser—0
Simultaneous Sparse Dictionary Learning and Pruning—0
Image Restoration with Locally Selected Class-Adapted Models—0
Hierarchical Piecewise-Constant Super-regions—0
A Geometric Approach to Color Image Regularization—0
Biologically Inspired Radio Signal Feature Extraction with Sparse Denoising Autoencoders—0
Automatic Image Annotation via Label Transfer in the Semantic Space—0
A Gaussian Mixture MRF for Model-Based Iterative Reconstruction with Applications to Low-Dose X-ray CT—0
Modified Weibull distribution for Biomedical signals denoising—0
Transport Analysis of Infinitely Deep Neural Network—0
Show:102550
← PrevPage 278 of 292Next →
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