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 5101–5125 of 7282 papers

TitleStatusHype
Segmented and Non-Segmented Stacked Denoising Autoencoder for Hyperspectral Band Reduction—0
SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness—0
Accelerated graph-based spectral polynomial filters—0
Seismic data denoising and deblending using deep learning—0
Seismic Data Interpolation via Denoising Diffusion Implicit Models with Coherence-corrected Resampling—0
Seizure Classification of EEG based on Wavelet Signal Denoising Using a Novel Channel Selection Algorithm—0
Select-and-Sample for Spike-and-Slab Sparse Coding—0
Selective Guidance: Are All the Denoising Steps of Guided Diffusion Important?—0
Selector-Enhancer: Learning Dynamic Selection of Local and Non-local Attention Operation for Speech Enhancement—0
Using Complex Wavelet Transform and Bilateral Filtering for Image Denoising—0
Using Convolutional Neural Networks for Denoising and Deblending of Marine Seismic Data—0
Self-Attention Generative Adversarial Network for Iterative Reconstruction of CT Images—0
Self-Calibrated Variance-Stabilizing Transformations for Real-World Image Denoising—0
Self-Committee Approach for Image Restoration Problems using Convolutional Neural Network—0
Self-Consistent Model-based Adaptation for Visual Reinforcement Learning—0
Accelerated graph-based nonlinear denoising filters—0
Self-Controlled Diffusion for Denoising in Scientific Imaging—0
Self-Denoising Neural Networks for Few Shot Learning—0
Self-Expressive Decompositions for Matrix Approximation and Clustering—0
Self-Induced Curriculum Learning in Neural Machine Translation—0
Self-Induced Curriculum Learning in Self-Supervised Neural Machine Translation—0
Self is the Best Learner: CT-free Ultra-Low-Dose PET Organ Segmentation via Collaborating Denoising and Segmentation Learning—0
Self-Organized Operational Neural Networks for Severe Image Restoration Problems—0
Self-supervised Bayesian Deep Learning for Image Denoising—0
Self-supervised Conformal Prediction for Uncertainty Quantification in Imaging Problems—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