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 5751–5775 of 7282 papers

TitleStatusHype
A Generative Adversarial Approach To ECG Synthesis And DenoisingCode0
Extensions to the Proximal Distance Method of Constrained Optimization—0
Denoising Click-evoked Otoacoustic Emission Signals by Optimal Shrinkage—0
Operational vs Convolutional Neural Networks for Image Denoising—0
Rank-one partitioning: formalization, illustrative examples, and a new cluster enhancing strategy—0
Self-Organized Operational Neural Networks for Severe Image Restoration Problems—0
PCB Defect Detection Using Denoising Convolutional Autoencoders—0
Mixed Noise Removal with Pareto Prior—0
Efficient Blind-Spot Neural Network Architecture for Image Denoising—0
A Critical Analysis of Patch Similarity Based Image Denoising Algorithms—0
Coupling BM3D with directional wavelet packets for image denoising—0
Community-Aware Graph Signal Processing—0
Unsupervised Hyperspectral Mixed Noise Removal Via Spatial-Spectral Constrained Deep Image Prior—0
PNEN: Pyramid Non-Local Enhanced Networks—0
Spectral independent component analysis with noise modeling for M/EEG source separation—0
Self-supervised Denoising via Diffeomorphic Template Estimation: Application to Optical Coherence Tomography—0
Generative Adversarial Networks for Robust Cryo-EM Image DenoisingCode0
Wavelet Denoising and Attention-based RNN-ARIMA Model to Predict Forex Price—0
Evolving Deep Convolutional Neural Networks for Hyperspectral Image Denoising—0
The Effect of Various Strengths of Noises and Data Augmentations on Classification of Short Single-Lead ECG Signals Using Deep Neural Networks—0
Unsupervised Image Restoration Using Partially Linear DenoisersCode0
Performance characterization of a novel deep learning-based MR image reconstruction pipeline—0
AdaIN-Switchable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising—0
Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance ImagesCode0
Powers of layers for image-to-image translation—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