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 2826–2850 of 7282 papers

TitleStatusHype
Few-shot point cloud reconstruction and denoising via learned Guassian splats renderings and fine-tuned diffusion featuresCode0
FFDNet: Toward a Fast and Flexible Solution for CNN based Image DenoisingCode0
FIND: Fine-tuning Initial Noise Distribution with Policy Optimization for Diffusion ModelsCode0
Few Clean Instances Help Denoising Distant SupervisionCode0
Few-shot Image Generation with Diffusion ModelsCode0
Can learning from natural image denoising be used for seismic data interpolation?Code0
FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow EstimationCode0
FEUNet: a flexible and effective U-shaped network for image denoisingCode0
Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure PredictionCode0
Finding Local Diffusion Schrödinger Bridge using Kolmogorov-Arnold NetworkCode0
Feature-Based Image Clustering and Segmentation Using WaveletsCode0
A new method for determining Wasserstein 1 optimal transport maps from Kantorovich potentials, with deep learning applicationsCode0
FDG-Diff: Frequency-Domain-Guided Diffusion Framework for Compressed Hazy Image RestorationCode0
Feature Enhancement with Deep Feature Losses for Speaker VerificationCode0
Can denoising diffusion probabilistic models generate realistic astrophysical fields?Code0
DiffPAD: Denoising Diffusion-based Adversarial Patch DecontaminationCode0
fastWDM3D: Fast and Accurate 3D Healthy Tissue InpaintingCode0
FDF: Flexible Decoupled Framework for Time Series Forecasting with Conditional Denoising and Polynomial ModelingCode0
DiffNorm: Self-Supervised Normalization for Non-autoregressive Speech-to-speech TranslationCode0
Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?Code0
DiffMotion: Speech-Driven Gesture Synthesis Using Denoising Diffusion ModelCode0
Fast Multi-grid Methods for Minimizing Curvature EnergyCode0
Fast Track to Winning Tickets: Repowering One-Shot Pruning for Graph Neural NetworksCode0
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET DenoisingCode0
Finding Local Diffusion Schrodinger Bridge using Kolmogorov-Arnold NetworkCode0
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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