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 4501–4525 of 7282 papers

TitleStatusHype
Online Deep Equilibrium Learning for Regularization by DenoisingCode1
Thunder: Thumbnail based Fast Lightweight Image Denoising Network—0
Decoder Denoising Pretraining for Semantic SegmentationCode1
Denoising-based image reconstruction from pixels located at non-integer positions—0
Flexible Diffusion Modeling of Long VideosCode1
A Coupling Enhancement Algorithm for ZrO2 Ceramic Bearing Ball Surface Defect Detection Based on Cartoon-texture Decomposition Model and Multi-Scale Filtering Method—0
Multi-Agent Feedback Enabled Neural Networks for Intelligent CommunicationsCode0
AutoJoin: Efficient Adversarial Training against Gradient-Free Perturbations for Robust Maneuvering via Denoising Autoencoder and Joint LearningCode0
Reliability-based Mesh-to-Grid Image Reconstruction—0
Planning with Diffusion for Flexible Behavior SynthesisCode3
A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2NoiseCode1
A Simple Yet Effective SVD-GCN for Directed GraphsCode0
HyperAid: Denoising in hyperbolic spaces for tree-fitting and hierarchical clusteringCode0
Masked Image Modeling with Denoising ContrastCode1
Deterministic training of generative autoencoders using invertible layersCode1
MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and InterpolationCode2
Evaluation of Transfer Learning for Polish with a Text-to-Text Model—0
Exploiting Social Media Content for Self-Supervised Style TransferCode0
A Survey on Hyperspectral Image Restoration: From the View of Low-Rank Tensor Approximation—0
Variational Quantum Compressed Sensing for Joint User and Channel State Acquisition in Grant-Free Device Access Systems—0
A Framework for CSI-Based Indoor Localization with 1D Convolutional Neural Networks—0
TOCH: Spatio-Temporal Object-to-Hand Correspondence for Motion Refinement—0
Low power communication signal enhancement method of Internet of things based on nonlocal mean denoising—0
Dense residual Transformer for image denoising—0
Simple Contrastive Graph Clustering—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