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 1626–1650 of 7282 papers

TitleStatusHype
Decoupled Video Generation with Chain of Training-free Diffusion Model Experts—0
PointDGMamba: Domain Generalization of Point Cloud Classification via Generalized State Space ModelCode0
General Intelligent Imaging and Uncertainty Quantification by Deterministic Diffusion Model—0
Controlled Learning of Pointwise Nonlinearities in Neural-Network-Like Architectures—0
IFH: a Diffusion Framework for Flexible Design of Graph Generative ModelsCode0
How Diffusion Models Learn to Factorize and Compose—0
CustomCrafter: Customized Video Generation with Preserving Motion and Concept Composition AbilitiesCode2
3D Photon Counting CT Image Super-Resolution Using Conditional Diffusion Model—0
Variance reduction of diffusion model's gradients with Taylor approximation-based control variate—0
Factor Adjusted Spectral Clustering for Mixture Models—0
Adapting MIMO video restoration networks to low latency constraints—0
An Evaluation of Deep Learning Models for Stock Market Trend PredictionCode1
First line of defense: A robust first layer mitigates adversarial attacksCode0
Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation—0
Detection-Driven Object Count Optimization for Text-to-Image Diffusion Models—0
Pixel Is Not A Barrier: An Effective Evasion Attack for Pixel-Domain Diffusion Models—0
MegaFusion: Extend Diffusion Models towards Higher-resolution Image Generation without Further TuningCode2
Hierarchical Attention Diffusion Networks with Object Priors for Video Change Detection—0
Denoising Plane Wave Ultrasound Images Using Diffusion Probabilistic Models—0
Perception-based multiplicative noise removal using SDEsCode0
Instruction-Based Molecular Graph Generation with Unified Text-Graph Diffusion ModelCode0
The Brittleness of AI-Generated Image Watermarking Techniques: Examining Their Robustness Against Visual Paraphrasing Attacks—0
Multi-Scale Representation Learning for Image Restoration with State-Space Model—0
Diffusion Model for Planning: A Systematic Literature Review—0
A lifted Bregman strategy for training unfolded proximal neural network Gaussian denoisers—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