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 5701–5725 of 7282 papers

TitleStatusHype
Learning Integrodifferential Models for Image Denoising—0
Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs—0
A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks—0
ENSURE: A General Approach for Unsupervised Training of Deep Image Reconstruction Algorithms—0
Tongji University Undergraduate Team for the VoxCeleb Speaker Recognition Challenge2020—0
Poisson Image Deconvolution by a Plug-and-Play Quantum Denoising Scheme—0
Independent versus truncated finite approximations for Bayesian nonparametric inference—0
Generalized Intersection Algorithms with Fixpoints for Image Decomposition Learning—0
Multiscale Optimal Filtering on the Sphere—0
The Benefit of Distraction: Denoising Remote Vitals Measurements using Inverse Attention—0
Image Denoising Using the Geodesics' Gramian of the Manifold Underlying Patch-Space—0
Optimum Codesign for Image Denoising Between Type-2 Fuzzy Identifier and Matrix Completion Denoiser—0
Diagnosing and Preventing Instabilities in Recurrent Video Processing—0
A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction—0
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
Global Adaptive Filtering Layer for Computer Vision—0
Active Tuning—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
RAR-U-Net: a Residual Encoder to Attention Decoder by Residual Connections Framework for Spine Segmentation under Noisy Labels—0
A Unified Plug-and-Play Framework for Effective Data Denoising and Robust Abstention—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