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 2976–3000 of 7282 papers

TitleStatusHype
Bootstrap3D: Improving Multi-view Diffusion Model with Synthetic Data—0
Anatomically and Metabolically Informed Diffusion for Unified Denoising and Segmentation in Low-Count PET Imaging—0
Additive Component Analysis—0
Accelerating Prostate Diffusion Weighted MRI using Guided Denoising Convolutional Neural Network: Retrospective Feasibility Study—0
DenseFormer: Learning Dense Depth Map from Sparse Depth and Image via Conditional Diffusion Model—0
Denosing Using Wavelets and Projections onto the L1-Ball—0
BOOT: Data-free Distillation of Denoising Diffusion Models with Bootstrapping—0
Boosting with Structural Sparsity: A Differential Inclusion Approach—0
Anatomical-Guided Attention Enhances Unsupervised PET Image Denoising Performance—0
DenoMAE2.0: Improving Denoising Masked Autoencoders by Classifying Local Patches—0
Boosting the Performance of Plug-and-Play Priors via Denoiser Scaling—0
denoiSplit: a method for joint microscopy image splitting and unsupervised denoising—0
Denoising Word Embeddings by Averaging in a Shared Space—0
An Artificial Intelligence Enabled Signature Estimation of Dual Wideband Systems in Ultra-Low Signal-to-Noise Ratio—0
Add and Thin: Diffusion for Temporal Point Processes—0
Denoising without access to clean data using a partitioned autoencoder—0
Denoising with a Joint-Embedding Predictive Architecture—0
Boosting of Image Denoising Algorithms—0
Denoising Weak Lensing Mass Maps with Deep Learning—0
Boosting Image Restoration via Priors from Pre-trained Models—0
An approach to image denoising using manifold approximation without clean images—0
Denoising via Repainting: an image denoising method using layer wise medical image repainting—0
Boosting Fast and High-Quality Speech Synthesis with Linear Diffusion—0
Denoising VAE as an Explainable Feature Reduction and Diagnostic Pipeline for Autism Based on Resting state fMRI—0
Denoising User-aware Memory Network for Recommendation—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