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 4851–4875 of 7282 papers

TitleStatusHype
Learning Task-Oriented Flows to Mutually Guide Feature Alignment in Synthesized and Real Video Denoising—0
2nd Place Solutions for UG2+ Challenge 2022 -- D^3Net for Mitigating Atmospheric Turbulence from Images—0
Riesz-Quincunx-UNet Variational Auto-Encoder for Satellite Image DenoisingCode0
Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling—0
Learning Better Masking for Better Language Model Pre-trainingCode0
Unsupervised Question Answering via Answer DiversifyingCode0
Reversing Skin Cancer Adversarial Examples by Multiscale Diffusive and Denoising Aggregation Mechanism—0
Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label PropagationCode0
Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure PredictionCode0
G2P-DDM: Generating Sign Pose Sequence from Gloss Sequence with Discrete Diffusion Model—0
A decomposition of book structure through ousiometric fluctuations in cumulative word-time—0
Enhancing Diffusion-Based Image Synthesis with Robust Classifier GuidanceCode0
Lifted Bregman Training of Neural Networks—0
CommitBART: A Large Pre-trained Model for GitHub Commits—0
Self-supervised training of deep denoisers in multi-coil MRI considering noise correlations—0
Uni6Dv2: Noise Elimination for 6D Pose Estimation—0
On a Mechanism Framework of Autoencoders—0
Recent Progress in Transformer-based Medical Image Analysis—0
Multilayer Fisher extreme learning machine for classification—0
Convergence of denoising diffusion models under the manifold hypothesis—0
A data-driven modular architecture with denoising autoencoders for health indicator construction in a manufacturing process—0
A Topological Loss Function: Image Denoising on a Low-Light Dataset—0
Denoising Induction Motor Sounds Using an Autoencoder—0
Image denoising in acoustic field microscopy—0
SciAnnotate: A Tool for Integrating Weak Labeling Sources for Sequence LabelingCode0
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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