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 2301–2325 of 7282 papers

TitleStatusHype
Med-gte-hybrid: A contextual embedding transformer model for extracting actionable information from clinical texts—0
Denoising, segmentation and volumetric rendering of optical coherence tomography angiography (OCTA) image using deep learning techniques: a review—0
Textured 3D Regenerative Morphing with 3D Diffusion Prior—0
EyeBench: A Call for More Rigorous Evaluation of Retinal Image EnhancementCode0
A Data-Driven Paradigm-Based Image Denoising and Mosaicking Approach for High-Resolution Acoustic Camera—0
Denoising Designs-inherited Search Framework for Image Denoising—0
Interleaved Gibbs Diffusion for Constrained Generation—0
Generalization error bound for denoising score matching under relaxed manifold assumption—0
RestoreGrad: Signal Restoration Using Conditional Denoising Diffusion Models with Jointly Learned Prior—0
Unsupervised CP-UNet Framework for Denoising DAS Data with Decay Noise—0
Guaranteed Conditional Diffusion: 3D Block-based Models for Scientific Data Compression—0
GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects—0
Is Noise Conditioning Necessary for Denoising Generative Models?—0
Contrast-Unity for Partially-Supervised Temporal Sentence Grounding—0
Inverse Flow and Consistency Models—0
Exploiting network optimization stability for enhanced PET image denoising using deep image prior—0
CLoCKDistill: Consistent Location-and-Context-aware Knowledge Distillation for DETRs—0
NeuroAMP: A Novel End-to-end General Purpose Deep Neural Amplifier for Personalized Hearing Aids—0
Rao-Blackwell Gradient Estimators for Equivariant Denoising Diffusion—0
OptimOTU: Taxonomically aware OTU clustering with optimized thresholds and a bioinformatics workflow for metabarcoding data—0
Self-Consistent Model-based Adaptation for Visual Reinforcement Learning—0
Residual Transformer Fusion Network for Salt and Pepper Image Denoising—0
E-MD3C: Taming Masked Diffusion Transformers for Efficient Zero-Shot Object Customization—0
Unleashing the Power of Large Language Model for Denoising Recommendation—0
BCDDM: Branch-Corrected Denoising Diffusion Model for Black Hole Image Generation—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