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 6101–6125 of 7282 papers

TitleStatusHype
Deep Manifold Prior—0
DeepMeshFlow: Content Adaptive Mesh Deformation for Robust Image Registration—0
Spatiotemporal singular value decomposition for denoising in photoacoustic imaging with low-energy excitation light source—0
Deep Metric Learning-Based Out-of-Distribution Detection with Synthetic Outlier Exposure—0
Deep MMD Gradient Flow without adversarial training—0
Deep Multi-contrast Cardiac MRI Reconstruction via vSHARP with Auxiliary Refinement Network—0
Deep Network for Capacitive ECG Denoising—0
Deep Network for Simultaneous Decomposition and Classification in UWB-SAR Imagery—0
Deep Networks as Denoising Algorithms: Sample-Efficient Learning of Diffusion Models in High-Dimensional Graphical Models—0
Deep network series for large-scale high-dynamic range imaging—0
Deep Neural Network-Based Quantized Signal Reconstruction for DOA Estimation—0
Deep neural network for fringe pattern filtering and normalisation—0
Deep neural networks-based denoising models for CT imaging and their efficacy—0
Deep neural networks for learning graph representations—0
Deep Neural Networks to Recover Unknown Physical Parameters from Oscillating Time Series—0
Deep Noise Suppression Maximizing Non-Differentiable PESQ Mediated by a Non-Intrusive PESQNet—0
Deep Noise Suppression With Non-Intrusive PESQNet Supervision Enabling the Use of Real Training Data—0
Deep Nonparametric Convexified Filtering for Computational Photography, Image Synthesis and Adversarial Defense—0
Deep Nonparametric Estimation of Intrinsic Data Structures by Chart Autoencoders: Generalization Error and Robustness—0
DeepPCR: Parallelizing Sequential Operations in Neural Networks—0
Deep Photon Mapping—0
Speaker-Independent Microphone Identification in Noisy Conditions—0
Deep Plug-and-play Prior for Low-rank Tensor Completion—0
Deep Point Cloud Reconstruction—0
Deep Point Set Resampling via Gradient Fields—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