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 2151–2175 of 7282 papers

TitleStatusHype
Accelerating Diffusion-based Combinatorial Optimization Solvers by Progressive Distillation—0
Dynamic Slimmable Denoising Network—0
Dynamic Try-On: Taming Video Virtual Try-on with Dynamic Attention Mechanism—0
E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce—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
A method of limiting performance loss of CNNs in noisy environments—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
Adaptively Denoising Proposal Collection for Weakly Supervised Object Localization—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
Practical Operator Sketching Framework for Accelerating Iterative Data-Driven Solutions in Inverse Problems—0
DENOISER: Rethinking the Robustness for Open-Vocabulary Action Recognition—0
BEVDiffuser: Plug-and-Play Diffusion Model for BEV Denoising with Ground-Truth Guidance—0
Better Generalization with On-the-fly Dataset Denoising—0
Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data—0
Adaptively Denoising Proposal Collection forWeakly Supervised Object Localization—0
Denoised Labels for Financial Time-Series Data via Self-Supervised Learning—0
Beta Sampling is All You Need: Efficient Image Generation Strategy for Diffusion Models using Stepwise Spectral Analysis—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