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 5901–5925 of 7282 papers

TitleStatusHype
Cross-denoising Network against Corrupted Labels in Medical Image Segmentation with Domain Shift—0
Cross-domain feature disentanglement for interpretable modeling of tumor microenvironment impact on drug response—0
Cross-Domain Image Conversion by CycleDM—0
Cross-domain Iterative Network for Simultaneous Denoising, Limited-angle Reconstruction, and Attenuation Correction of Low-dose Cardiac SPECT—0
Cross-Domain Lossy Compression as Optimal Transport with an Entropy Bottleneck—0
Cross-domain Recommendation via Deep Domain Adaptation—0
Cross-Frequency Implicit Neural Representation with Self-Evolving Parameters—0
Crossfusor: A Cross-Attention Transformer Enhanced Conditional Diffusion Model for Car-Following Trajectory Prediction—0
Cross-Image Attention for Zero-Shot Appearance Transfer—0
Sparse Factorization Layers for Neural Networks with Limited Supervision—0
Crosslingual Embeddings are Essential in UNMT for Distant Languages: An English to IndoAryan Case Study—0
CrossMatch: Source-Free Domain Adaptive Semantic Segmentation via Cross-Modal Consistency Training—0
Cross-Modal Denoising: A Novel Training Paradigm for Enhancing Speech-Image Retrieval—0
Cross-Patch Graph Convolutional Network for Image Denoising—0
Cross-Scale Residual Network for Multiple Tasks:Image Super-resolution, Denoising, and Deblocking—0
Analyzing the Weighted Nuclear Norm Minimization and Nuclear Norm Minimization based on Group Sparse Representation—0
CrossViewDiff: A Cross-View Diffusion Model for Satellite-to-Street View Synthesis—0
Analyzing noise in autoencoders and deep networks—0
Crowd Counting in Harsh Weather using Image Denoising with Pix2Pix GANs—0
CrowdMoGen: Zero-Shot Text-Driven Collective Motion Generation—0
Cryo-Electron Microscopy Image Analysis Using Multi-Frequency Vector Diffusion Maps—0
Sparse Filtered SIRT for Electron Tomography—0
CSI-fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network—0
CSI-PPPNet: A One-Sided One-for-All Deep Learning Framework for Massive MIMO CSI Feedback—0
CS-PCN: Context-Space Progressive Collaborative Network for Image Denoising—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