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 3251–3300 of 7282 papers

TitleStatusHype
Equivariant plug-and-play image reconstruction—0
Graph Based Sinogram Denoising for Tomographic Reconstructions—0
Equivariant Denoisers for Image Restoration—0
Convergent regularization in inverse problems and linear plug-and-play denoisers—0
ASD-Diffusion: Anomalous Sound Detection with Diffusion Models—0
Graph Convolutional Neural Networks for Automated Echocardiography View Recognition: A Holistic Approach—0
Graph Defense Diffusion Model—0
EP-CFG: Energy-Preserving Classifier-Free Guidance—0
Autoregressive Score Matching—0
Graph Differentiable Architecture Search with Structure Learning—0
Graph Feature Gating Networks—0
Graph filtering over expanding graphs—0
多樣訊雜比之訓練語料於降噪自動編碼器其語音強化功能之初步研究 (A Preliminary Study of Various SNR-level Training Data in the Denoising Auto-encoder (DAE) Technique for Speech Enhancement) [In Chinese]—0
AdvLogo: Adversarial Patch Attack against Object Detectors based on Diffusion Models—0
Image-Conditional Diffusion Transformer for Underwater Image Enhancement—0
Graph Laplacian Regularization for Image Denoising: Analysis in the Continuous Domain—0
Graph Neural Networks and Differential Equations: A hybrid approach for data assimilation of fluid flows—0
Entrywise Inference for Missing Panel Data: A Simple and Instance-Optimal Approach—0
Graph Representation Learning with Diffusion Generative Models—0
Graph Sanitation with Application to Node Classification—0
Entropy stable conservative flux form neural networks—0
Graphs as Tools to Improve Deep Learning Methods—0
Graph Signal Restoration Using Nested Deep Algorithm Unrolling—0
Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks—0
Convergence rates for pretraining and dropout: Guiding learning parameters using network structure—0
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