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 3201–3225 of 7282 papers

TitleStatusHype
Global Adaptive Filtering Layer for Computer Vision—0
Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learning—0
Denoising and Selecting Pseudo-Heatmaps for Semi-Supervised Human Pose Estimation—0
Denoising and Segmentation of Epigraphical Scripts—0
Denoising and Reconstruction of Nonlinear Dynamics using Truncated Reservoir Computing—0
Beyond Generation: A Diffusion-based Low-level Feature Extractor for Detecting AI-generated Images—0
Denoising and Optical and SAR Image Classifications Based on Feature Extraction and Sparse Representation—0
Beyond Fixed Horizons: A Theoretical Framework for Adaptive Denoising Diffusions—0
A Missing Information Loss function for implicit feedback datasets—0
Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising—0
Accelerating Diffusion-based Combinatorial Optimization Solvers by Progressive Distillation—0
3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network—0
Denoising and feature extraction in photoemission spectra with variational auto-encoder neural networks—0
Denoising and Covariance Estimation of Single Particle Cryo-EM Images—0
Denoising and compression in wavelet domain via projection onto approximation coefficients—0
Denoising and Completion of 3D Data via Multidimensional Dictionary Learning—0
Beyond Classification: Evaluating Diffusion Denoised Smoothing for Security-Utility Trade off—0
A method of limiting performance loss of CNNs in noisy environments—0
Denoising and Alignment: Rethinking Domain Generalization for Multimodal Face Anti-Spoofing—0
Denoising and Adaptive Online Vertical Federated Learning for Sequential Multi-Sensor Data in Industrial Internet of Things—0
Beyond Brightness Constancy: Learning Noise Models for Optical Flow—0
Denoising after Entropy-based Debiasing A Robust Training Method for Dataset Bias with Noisy Labels—0
Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data—0
AMC-Net: An Effective Network for Automatic Modulation Classification—0
Adaptively Denoising Proposal Collection for Weakly Supervised Object Localization—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