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 6501–6525 of 7282 papers

TitleStatusHype
A locally statistical active contour model for SAR image segmentation can be solved by denoising algorithms—0
Steering Rectified Flow Models in the Vector Field for Controlled Image Generation—0
Almost Unsupervised Text to Speech and Automatic Speech Recognition—0
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous and Instruction-guided Driving—0
Diffusion-EXR: Controllable Review Generation for Explainable Recommendation via Diffusion Models—0
Diffusion Gaussian Mixture Audio Denoise—0
Diffusion Generative Inverse Design—0
Diffusion-Generative Multi-Fidelity Learning for Physical Simulation—0
Diffusion Image Prior—0
Diffusion Implicit Policy for Unpaired Scene-aware Motion Synthesis—0
4D X-Ray CT Reconstruction using Multi-Slice Fusion—0
DiffusionMat: Alpha Matting as Sequential Refinement Learning—0
VideoScene: Distilling Video Diffusion Model to Generate 3D Scenes in One Step—0
All-Optical Nonlinear Diffractive Deep Network for Ultrafast Image Denoising—0
A ground-based dataset and a diffusion model for on-orbit low-light image enhancement—0
Step Saver: Predicting Minimum Denoising Steps for Diffusion Model Image Generation—0
Diffusion Model Based Probabilistic Day-ahead Load Forecasting—0
Diffusion Model Based Resource Allocation Strategy in Ultra-Reliable Wireless Networked Control Systems—0
ST-FL: Style Transfer Preprocessing in Federated Learning for COVID-19 Segmentation—0
Diffusion Model Conditioning on Gaussian Mixture Model and Negative Gaussian Mixture Gradient—0
Diffusion Model for Generative Image Denoising—0
Diffusion Model for Planning: A Systematic Literature Review—0
Diffusion Model from Scratch—0
Video Summarization using Denoising Diffusion Probabilistic Model—0
Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction—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