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 2476–2500 of 7282 papers

TitleStatusHype
A Unified Plug-and-Play Framework for Effective Data Denoising and Robust Abstention—0
A Two-Stage Progressive Pre-training using Multi-Modal Contrastive Masked Autoencoders—0
Decoupled Diffusion Sparks Adaptive Scene Generation—0
A unified framework of non-local parametric methods for image denoising—0
Exploring Distributional Representations and Machine Translation for Aspect-based Cross-lingual Sentiment Classification.—0
Deconstructing the Ladder Network Architecture—0
A Unified Framework for Multi-Sensor HDR Video Reconstruction—0
Deconstructing Principal Component Analysis Using a Data Reconciliation Perspective—0
A Unified Conditional Framework for Diffusion-based Image Restoration—0
Aircraft Radar Altimeter Interference Mitigation Through a CNN-Layer Only Denoising Autoencoder Architecture—0
Exploring ensembles and uncertainty minimization in denoising networks—0
DeCompress: Denoising via Neural Compression—0
A Unified Approach to Analysis and Design of Denoising Markov Models—0
Nonlocal Patches based Gaussian Mixture Model for Image Inpainting—0
Decomposition-based and Interference Perception for Infrared and Visible Image Fusion in Complex Scenes—0
Decomposition Ascribed Synergistic Learning for Unified Image Restoration—0
Decomposable Nonlocal Tensor Dictionary Learning for Multispectral Image Denoising—0
Augmented Robust PCA For Foreground-Background Separation on Noisy, Moving Camera Video—0
AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models—0
Exploring Continual Learning of Diffusion Models—0
Exploring Denoised Cross-Video Contrast for Weakly-Supervised Temporal Action Localization—0
Exploring Diffusion with Test-Time Training on Efficient Image Restoration—0
Factorized Diffusion: Perceptual Illusions by Noise Decomposition—0
Fast, nonlocal and neural: a lightweight high quality solution to image denoising—0
Flexible Multi-layer Sparse Approximations of Matrices and Applications—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