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 5976–6000 of 7282 papers

TitleStatusHype
Segmentation of Retinal Low-Cost Optical Coherence Tomography Images using Deep Learning—0
Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio—0
Model-Based Deep Learning for Reconstruction of Joint k-q Under-sampled High Resolution Diffusion MRI—0
HRFA: High-Resolution Feature-based Attack—0
Depth Completion Using a View-constrained Deep Prior—0
CNN-Based Real-Time Parameter Tuning for Optimizing Denoising Filter PerformanceCode0
Image denoising via K-SVD with primal-dual active set algorithm—0
Sinogram super-resolution and denoising convolutional neural network (SRCN) for limited data photoacoustic tomography—0
Adaptive Direction-Guided Structure Tensor Total Variation—0
Masking schemes for universal marginalisers—0
Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks—0
Towards Deep Unsupervised SAR Despeckling with Blind-Spot Convolutional Neural Networks—0
Autoencoders as Weight Initialization of Deep Classification Networks for Cancer versus Cancer Studies—0
Self-Supervised Fast Adaptation for Denoising via Meta-Learning—0
Limited Angle Tomography for Transmission X-Ray Microscopy Using Deep Learning—0
Learning Generative Models using Denoising Density EstimatorsCode0
Hypergraph Spectral Analysis and Processing in 3D Point Cloud—0
Speech Enhancement based on Denoising Autoencoder with Multi-branched EncodersCode0
Image Speckle Noise Denoising by a Multi-Layer Fusion Enhancement Method based on Block Matching and 3D Filtering—0
TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising—0
InSAR Phase Denoising: A Review of Current Technologies and Future Directions—0
A Machine Learning Imaging Core using Separable FIR-IIR Filters—0
First image then video: A two-stage network for spatiotemporal video denoisingCode0
DeepBeat: A multi-task deep learning approach to assess signal quality and arrhythmia detection in wearable devices—0
A Total Variation Denoising Method Based on Median Filter and Phase Consistency—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