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 5951–5975 of 7282 papers

TitleStatusHype
D²Net: A Denoising and Dereverberation Network Based on Two-branch Encoder and Dual-path Transformer—0
Sparse-View X-Ray CT Reconstruction Using _1 Prior with Learned Transform—0
Sparse wavefield reconstruction and denoising with boostlets—0
D3DR: Lighting-Aware Object Insertion in Gaussian Splatting—0
Sparsity and Coefficient Permutation Based Two-Domain AMP for Image Block Compressed Sensing—0
Sparsity-Aware Optimal Transport for Unsupervised Restoration Learning—0
Sparsity Based Autoencoders for Denoising Cluttered Radar Signatures—0
Sparsity Based Methods for Overparameterized Variational Problems—0
DAEs for Linear Inverse Problems: Improved Recovery with Provable Guarantees—0
Sparsity Based Poisson Denoising with Dictionary Learning—0
DAGSM: Disentangled Avatar Generation with GS-enhanced Mesh—0
Sparta: Spatially Attentive and Adversarially Robust Activations—0
Analytical Discovery of Manifold with Machine Learning—0
Dancing under the stars: video denoising in starlight—0
Dantzig Selector with an Approximately Optimal Denoising Matrix and its Application to Reinforcement Learning—0
DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation—0
Analysis Operator Learning and Its Application to Image Reconstruction—0
DarkFarseer: Inductive Spatio-temporal Kriging via Hidden Style Enhancement and Sparsity-Noise Mitigation—0
DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation—0
Evaluation of a Low-Cost Single-Lead ECG Module for Vascular Ageing Prediction and Studying Smoking-induced Changes in ECG—0
Data Augmentation for Diverse Voice Conversion in Noisy Environments—0
Spatial-and-Frequency-aware Restoration method for Images based on Diffusion Models—0
Data augmentation versus noise compensation for x- vector speaker recognition systems in noisy environments—0
Data Augmentation via Diffusion Model to Enhance AI Fairness—0
Data Augmentation with Diffusion Models for Colon Polyp Localization on the Low Data Regime: How much real data is enough?—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