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 2551–2575 of 7282 papers

TitleStatusHype
LazyDiT: Lazy Learning for the Acceleration of Diffusion Transformers—0
Consistent Diffusion: Denoising Diffusion Model with Data-Consistent Training for Image Restoration—0
Unsupervised Region-Based Image Editing of Denoising Diffusion Models—0
3D MedDiffusion: A 3D Medical Diffusion Model for Controllable and High-quality Medical Image Generation—0
Bilevel Learning with Inexact Stochastic GradientsCode0
SPADE: Spectroscopic Photoacoustic Denoising using an Analytical and Data-free Enhancement Framework—0
MOVIS: Enhancing Multi-Object Novel View Synthesis for Indoor Scenes—0
AsymRnR: Video Diffusion Transformers Acceleration with Asymmetric Reduction and Restoration—0
Probabilities-Informed Machine Learning—0
Quantifying Climate Change Impacts on Renewable Energy Generation: A Super-Resolution Recurrent Diffusion Model—0
Machine Learning-Based Automated Assessment of Intracorporeal Suturing in Laparoscopic Fundoplication—0
DynamicScaler: Seamless and Scalable Video Generation for Panoramic Scenes—0
Missing data imputation for noisy time-series data and applications in healthcare—0
Rapid Reconstruction of Extremely Accelerated Liver 4D MRI via Chained Iterative Refinement—0
Generative Modeling with DiffusionCode0
Fast and Robust Visuomotor Riemannian Flow Matching Policy—0
Diffusion Model from Scratch—0
Diffusion-based Method for Satellite Pattern-of-Life Identification—0
SuperMark: Robust and Training-free Image Watermarking via Diffusion-based Super-Resolution—0
FaceShield: Defending Facial Image against Deepfake Threats—0
SnapGen-V: Generating a Five-Second Video within Five Seconds on a Mobile Device—0
EP-CFG: Energy-Preserving Classifier-Free Guidance—0
Self-Consistent Nested Diffusion Bridge for Accelerated MRI Reconstruction—0
Dynamic Try-On: Taming Video Virtual Try-on with Dynamic Attention Mechanism—0
Inference-Time Diffusion Model DistillationCode0
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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