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 3601–3625 of 7282 papers

TitleStatusHype
Graph Chirp Signal and Graph Fractional Vertex-Frequency Energy Distribution—0
Graph Based Sinogram Denoising for Tomographic Reconstructions—0
Implicit neural representations for end-to-end PET reconstruction—0
Implicit Regression in Subspace for High-Sensitivity CEST Imaging—0
Graph-Based Manifold Frequency Analysis for Denoising—0
Deep Learning-Based Extended Target Tracking in ISAC Systems—0
Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement—0
Graph-based denoising for time-varying point clouds—0
Deep learning based electrical noise removal enables high spectral optoacoustic contrast in deep tissue—0
Improved DDIM Sampling with Moment Matching Gaussian Mixtures—0
GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects—0
Improved Detection of Adversarial Images Using Deep Neural Networks—0
Deep Learning-based Denoising of Mammographic Images using Physics-driven Data Augmentation—0
Autonomous Point Cloud Segmentation for Power Lines Inspection in Smart Grid—0
A Light Label Denoising Method with the Internal Data Guidance—0
Improved functional MRI activation mapping in white matter through diffusion-adapted spatial filtering—0
Graffe: Graph Representation Learning via Diffusion Probabilistic Models—0
Gradual training of deep denoising auto encoders—0
Gradual Training Method for Denoising Auto Encoders—0
Improved Quasi-Recurrent Neural Network for Hyperspectral Image Denoising—0
Gradpaint: Gradient-Guided Inpainting with Diffusion Models—0
Improved Sparse Low-Rank Matrix Estimation—0
Gradient Statistics Aware Power Control for Over-the-Air Federated Learning—0
Gradient-Guided Conditional Diffusion Models for Private Image Reconstruction: Analyzing Adversarial Impacts of Differential Privacy and Denoising—0
Deep Learning Based Channel Covariance Matrix Estimation with User Location and Scene Images—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