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 5826–5850 of 7282 papers

TitleStatusHype
Inexact Derivative-Free Optimization for Bilevel LearningCode0
The Gaussian Transform—0
Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Network—0
Unified Representation Learning for Efficient Medical Image Analysis—0
Concatenated Attention Neural Network for Image Restoration—0
Cross-denoising Network against Corrupted Labels in Medical Image Segmentation with Domain Shift—0
Localized Spectral Graph Filter Frames: A Unifying Framework, Survey of Design Considerations, and Numerical Comparison (Extended Cut)—0
Noise2Inpaint: Learning Referenceless Denoising by Inpainting Unrolling—0
SIMBA: Scalable Inversion in Optical Tomography using Deep Denoising Priors—0
Gaussian Gated Linear NetworksCode0
Supervised Learning of Sparsity-Promoting Regularizers for Denoising—0
Learning Neural Light Transport—0
Cross-model Back-translated Distillation for Unsupervised Machine TranslationCode0
The Neural Tangent Link Between CNN Denoisers and Non-Local Filters—0
SeqXFilter: A Memory-efficient Denoising Filter for Dynamic Vision Sensors—0
Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising—0
Phase-aware Single-stage Speech Denoising and Dereverberation with U-NetCode0
Separating Particulate Matter From a Single Microscopic Image—0
A Statistical Approach to Signal Denoising Based on Data-driven Multiscale Representation—0
Probabilistic self-learning framework for Low-dose CT Denoising—0
Hyperspectral Image Denoising via Global Spatial-Spectral Total Variation Regularized Nonconvex Local Low-Rank Tensor Approximation—0
Pattern Denoising in Molecular Associative Memory using Pairwise Markov Random Field Models—0
Survey: Machine Learning in Production Rendering—0
Recovery of surfaces and functions in high dimensions: sampling theory and links to neural networks—0
Bayesian Conditional GAN for MRI Brain Image Synthesis—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