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 2876–2900 of 7282 papers

TitleStatusHype
FDiff-Fusion:Denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation—0
Cooperative Denoising for Distantly Supervised Relation Extraction—0
FDS: Frequency-Aware Denoising Score for Text-Guided Latent Diffusion Image Editing—0
A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration—0
Examining Joint Demosaicing and Denoising for Single-, Quad-, and Nona-Bayer Patterns—0
Exact Sparse Orthogonal Dictionary Learning—0
Cooling-Guide Diffusion Model for Battery Cell Arrangement—0
Feature Binding with Category-Dependant MixUp for Semantic Segmentation and Adversarial Robustness—0
Feature Denoising Diffusion Model for Blind Image Quality Assessment—0
Multi-Scale Denoising in the Feature Space for Low-Light Instance Segmentation—0
Cycle3D: High-quality and Consistent Image-to-3D Generation via Generation-Reconstruction Cycle—0
EWT: Efficient Wavelet-Transformer for Single Image Denoising—0
Feature-guided score diffusion for sampling conditional densities—0
Feature Learning from Incomplete EEG with Denoising Autoencoder—0
Feature Losses for Adversarial Robustness—0
Convolution Type of Metaplectic Cohen's Distribution Time-Frequency Analysis Theory, Method and Technology—0
Feature Prioritization and Regularization Improve Standard Accuracy and Adversarial Robustness—0
Feature vector regularization in machine learning—0
6D-Diff: A Keypoint Diffusion Framework for 6D Object Pose Estimation—0
Diffusion Transformer-based Universal Dose Denoising for Pencil Beam Scanning Proton Therapy—0
Federated Discrete Denoising Diffusion Model for Molecular Generation with OpenFL—0
Generative AI Models for Different Steps in Architectural Design: A Literature Review—0
Federated Learning for Medical Image Classification: A Comprehensive Benchmark—0
Generative artificial intelligence in ophthalmology: multimodal retinal images for the diagnosis of Alzheimer's disease with convolutional neural networks—0
EVRNet: Efficient Video Restoration on Edge Devices—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