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 5726–5750 of 7282 papers

TitleStatusHype
AR-DAE: Towards Unbiased Neural Entropy Gradient EstimationCode1
Understanding Graph Neural Networks from Graph Signal Denoising PerspectivesCode1
Neural Sparse Representation for Image RestorationCode1
Denoising Implicit Feedback for RecommendationCode1
Learning Neural Light Transport—0
A zero-inflated gamma model for deconvolved calcium imaging tracesCode1
The Neural Tangent Link Between CNN Denoisers and Non-Local Filters—0
Cross-model Back-translated Distillation for Unsupervised Machine TranslationCode0
Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting SurfacesCode1
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
Self2Self With Dropout: Learning Self-Supervised Denoising From Single ImageCode1
Separating Particulate Matter From a Single Microscopic Image—0
Joint Demosaicing and Denoising With Self GuidanceCode1
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
Recovery of surfaces and functions in high dimensions: sampling theory and links to neural networks—0
Survey: Machine Learning in Production Rendering—0
Bayesian Conditional GAN for MRI Brain Image Synthesis—0
Deep Learning-based Modulation Detection for NOMA Systems—0
MIMO Speech Compression and Enhancement Based on Convolutional Denoising Autoencoder—0
Lite Audio-Visual Speech EnhancementCode1
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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