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 3251–3275 of 7282 papers

TitleStatusHype
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
Graph Based Sinogram Denoising for Tomographic Reconstructions—0
AdvLogo: Adversarial Patch Attack against Object Detectors based on Diffusion Models—0
IC3D: Image-Conditioned 3D Diffusion for Shape Generation—0
Entrywise Inference for Missing Panel Data: A Simple and Instance-Optimal Approach—0
Entropy stable conservative flux form neural networks—0
Graph Defense Diffusion Model—0
Convergence rates for pretraining and dropout: Guiding learning parameters using network structure—0
Autoregressive Score Matching—0
Graph Differentiable Architecture Search with Structure Learning—0
Graph Feature Gating Networks—0
Graph filtering over expanding graphs—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
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
Convergence of gradient based pre-training in Denoising autoencoders—0
Graph Representation Learning with Diffusion Generative Models—0
Graph Sanitation with Application to Node Classification—0
Ensemble Noise Simulation to Handle Uncertainty about Gradient-based Adversarial Attacks—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
DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations—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