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 5301–5325 of 7282 papers

TitleStatusHype
Feature-Align Network with Knowledge Distillation for Efficient Denoising—0
Image denoising using complex-valued deep CNNCode0
Self-supervised Low Light Image Enhancement and DenoisingCode1
Unsupervised dynamic modeling of medical image transformationCode0
Should EBMs model the energy or the score?—0
Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-CaseCode1
Hyperspectral Denoising Using Unsupervised Disentangled Spatio-Spectral Deep Priors—0
Synergy Between Semantic Segmentation and Image Denoising via Alternate Boosting—0
Deep learning based electrical noise removal enables high spectral optoacoustic contrast in deep tissue—0
Handling Background Noise in Neural Speech Generation—0
Uncertainty-aware Generalized Adaptive CycleGANCode1
Denoising Higher-order Moments for Blind Digital Modulation Identification in Multiple-antenna SystemsCode1
EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks—0
Spatial-temporal switching estimators for imaging locally concentrated dynamics—0
ISCL: Interdependent Self-Cooperative Learning for Unpaired Image DenoisingCode1
Interpretable Stability Bounds for Spectral Graph Filters—0
Improved Denoising Diffusion Probabilistic ModelsCode3
NFCNN: Toward a Noise Fusion Convolutional Neural Network for Image Denoising—0
Training Stacked Denoising Autoencoders for Representation Learning—0
Context-Aware Prosody Correction for Text-Based Speech Editing—0
Joint self-supervised blind denoising and noise estimationCode1
A Sub-band Approach to Deep Denoising Wavelet Networks and a Frequency-adaptive Loss for Perceptual Quality—0
Orthogonal Features-based EEG Signal Denoising using Fractionally Compressed AutoEncoder—0
Intermediate Layer Optimization for Inverse Problems using Deep Generative ModelsCode1
Learning from Natural Noise to Denoise Micro-Doppler Spectrogram—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