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

Showing 28012825 of 7282 papers

TitleStatusHype
DiffuPose: Monocular 3D Human Pose Estimation via Denoising Diffusion Probabilistic ModelCode0
CAT Pruning: Cluster-Aware Token Pruning For Text-to-Image Diffusion ModelsCode0
Fingerprint Presentation Attack Detection by Channel-wise Feature DenoisingCode0
Fine-grained Forecasting Models Via Gaussian Process Blurring EffectCode0
Finding Local Diffusion Schrodinger Bridge using Kolmogorov-Arnold NetworkCode0
Accurate Segmentation of Optic Disc And Cup from Multiple Pseudo-labels by Noise-aware LearningCode0
Fine-grained Contrastive Learning for Relation ExtractionCode0
An Extended Framework for Marginalized Domain AdaptationCode0
FIND: Fine-tuning Initial Noise Distribution with Policy Optimization for Diffusion ModelsCode0
Finding Local Diffusion Schrödinger Bridge using Kolmogorov-Arnold NetworkCode0
FFDNet: Toward a Fast and Flexible Solution for CNN based Image DenoisingCode0
Fast Algorithm for Constrained Linear Inverse ProblemsCode0
Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure PredictionCode0
Fast and Differentiable Message Passing on Pairwise Markov Random FieldsCode0
Few-shot point cloud reconstruction and denoising via learned Guassian splats renderings and fine-tuned diffusion featuresCode0
A denoised Mean Teacher for domain adaptive point cloud registrationCode0
FEUNet: a flexible and effective U-shaped network for image denoisingCode0
Few Clean Instances Help Denoising Distant SupervisionCode0
Few-shot Image Generation with Diffusion ModelsCode0
Periodic Materials Generation using Text-Guided Joint Diffusion ModelCode0
DiffSmooth: Certifiably Robust Learning via Diffusion Models and Local SmoothingCode0
Can We Transfer Noise Patterns? A Multi-environment Spectrum Analysis Model Using Generated CasesCode0
A New Multi-Picture Architecture for Learned Video Deinterlacing and Demosaicing with Parallel Deformable Convolution and Self-Attention BlocksCode0
Feature-Based Image Clustering and Segmentation Using WaveletsCode0
Feature Enhancement with Deep Feature Losses for Speaker VerificationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified