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 5551–5575 of 7282 papers

TitleStatusHype
Diagnosing and Preventing Instabilities in Recurrent Video Processing—0
Denoising Multi-Source Weak Supervision for Neural Text ClassificationCode1
A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction—0
Denoising Diffusion Implicit ModelsCode2
Multi-task Learning for Multilingual Neural Machine Translation—0
Video Anomaly Detection Using Pre-Trained Deep Convolutional Neural Nets and Context Mining—0
A Unified View on Graph Neural Networks as Graph Signal DenoisingCode0
Spatial Frequency Bias in Convolutional Generative Adversarial Networks—0
Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors—0
Active Tuning—0
Global Adaptive Filtering Layer for Computer Vision—0
Weight Encode Reconstruction Network for Computed Tomography in a Semi-Case-Wise and Learning-Based Way—0
Deep Learning-based Symbolic Indoor Positioning using the Serving eNodeB—0
G-SimCLR: Self-Supervised Contrastive Learning with Guided Projection via Pseudo LabellingCode1
High-throughput molecular imaging via deep learning enabled Raman spectroscopyCode1
RAR-U-Net: a Residual Encoder to Attention Decoder by Residual Connections Framework for Spine Segmentation under Noisy Labels—0
Blind Image Super-Resolution with Spatial Context Hallucination—0
G-SimCLR : Self-Supervised Contrastive Learning with Guided Projection via Pseudo LabellingCode1
A Unified Plug-and-Play Framework for Effective Data Denoising and Robust Abstention—0
EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoisingCode1
Alternating minimization for a single step TV-Stokes model for image denoising—0
Multidimensional TV-Stokes for image processing—0
Iterative regularization algorithms for image denoising with the TV-Stokes model—0
Independent finite approximations for Bayesian nonparametric inference—0
A Generative Adversarial Approach with Residual Learning for Dust and Scratches Artifacts Removal—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