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 2501–2525 of 7282 papers

TitleStatusHype
Improving Diffusion Models for Inverse Problems Using Optimal Posterior CovarianceCode2
Denoising Diffusion-Based Control of Nonlinear SystemsCode0
Decomposition-based and Interference Perception for Infrared and Visible Image Fusion in Complex Scenes—0
DiffVein: A Unified Diffusion Network for Finger Vein Segmentation and Authentication—0
Analyzing Neural Network-Based Generative Diffusion Models through Convex Optimization—0
Code Representation Learning At Scale—0
LIR: A Lightweight Baseline for Image RestorationCode1
Cross-view Masked Diffusion Transformers for Person Image SynthesisCode2
Can Shape-Infused Joint Embeddings Improve Image-Conditioned 3D Diffusion?—0
Plug-and-Play image restoration with Stochastic deNOising REgularizationCode1
Signal Quality Auditing for Time-series Data—0
AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video DataCode4
Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman SpectraCode0
AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction ErrorCode2
Diffusion Model Compression for Image-to-Image Translation—0
Spatial-and-Frequency-aware Restoration method for Images based on Diffusion Models—0
BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane ExtrapolationCode2
InstructIR: High-Quality Image Restoration Following Human InstructionsCode4
Sliced Wasserstein with Random-Path Projecting DirectionsCode0
Spatial-Aware Latent Initialization for Controllable Image Generation—0
EventF2S: Asynchronous and Sparse Spiking AER Framework using Neuromorphic-Friendly Algorithm—0
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation—0
Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalization—0
Diffusion-based Graph Generative MethodsCode1
Data-Driven Estimation of the False Positive Rate of the Bayes Binary Classifier via Soft Labels—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