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 3276–3300 of 7282 papers

TitleStatusHype
Graph Spatio-Spectral Total Variation Model for Hyperspectral Image Denoising—0
Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising—0
Graph with Sequence: Broad-Range Semantic Modeling for Fake News Detection—0
Convergence of the denoising diffusion probabilistic models for general noise schedules—0
ENSURE: A General Approach for Unsupervised Training of Deep Image Reconstruction Algorithms—0
Convergence of score-based generative modeling for general data distributions—0
Deep Learning Improves Contrast in Low-Fluence Photoacoustic Imaging—0
Convergence of gradient based pre-training in Denoising autoencoders—0
Ensemble Noise Simulation to Handle Uncertainty about Gradient-based Adversarial Attacks—0
DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations—0
Group Sparse Coding for Image Denoising—0
YOND: Practical Blind Raw Image Denoising Free from Camera-Specific Data Dependency—0
Group Sparsity Residual Constraint for Image Denoising—0
Group Sparsity Residual with Non-Local Samples for Image Denoising—0
GSD: View-Guided Gaussian Splatting Diffusion for 3D Reconstruction—0
Multiscale Structure Guided Diffusion for Image Deblurring—0
Image denoising and restoration with CNN-LSTM Encoder Decoder with Direct Attention—0
GSNs : Generative Stochastic Networks—0
Guaranteed Conditional Diffusion: 3D Block-based Models for Scientific Data Compression—0
Enhancing Underwater Image via Adaptive Color and Contrast Enhancement, and Denoising—0
Enhancing the prediction of disease outcomes using electronic health records and pretrained deep learning models—0
Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions—0
Deeply Cascaded U-Net for Multi-Task Image Processing—0
Guided Conditional Diffusion Classifier (ConDiff) for Enhanced Prediction of Infection in Diabetic Foot Ulcers—0
Enhancing Text-to-Image Diffusion Transformer via Split-Text Conditioning—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