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 1051–1075 of 7282 papers

TitleStatusHype
Mask prior-guided denoising diffusion improves inverse protein foldingCode1
Mobile Video Diffusion—0
Video Motion Transfer with Diffusion TransformersCode2
Modeling Dual-Exposure Quad-Bayer Patterns for Joint Denoising and DeblurringCode1
A Progressive Image Restoration Network for High-order Degradation Imaging in Remote Sensing—0
Paired Wasserstein Autoencoders for Conditional Sampling—0
Anomaly detection using Diffusion-based methods—0
Fast Track to Winning Tickets: Repowering One-Shot Pruning for Graph Neural NetworksCode0
ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion TransformerCode1
Efficiency Meets Fidelity: A Novel Quantization Framework for Stable Diffusion—0
Echocardiography to Cardiac MRI View Transformation for Real-Time Blind Restoration—0
You KAN Do It in a Single Shot: Plug-and-Play Methods with Single-Instance Priors—0
Generative Lines Matching Models—0
ASGDiffusion: Parallel High-Resolution Generation with Asynchronous Structure Guidance—0
A CT Image Denoising Method Based on Projection Domain Feature—0
Around the World in 80 Timesteps: A Generative Approach to Global Visual GeolocationCode3
Diverse Score Distillation—0
CARP: Visuomotor Policy Learning via Coarse-to-Fine Autoregressive Prediction—0
BudgetFusion: Perceptually-Guided Adaptive Diffusion Models—0
FlexDiT: Dynamic Token Density Control for Diffusion TransformerCode1
Accelerating Video Diffusion Models via Distribution Matching—0
Enhanced 3D Generation by 2D Editing—0
3D-Consistent Image Inpainting with Diffusion Models—0
Tiny Object Detection with Single Point Supervision—0
Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling—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