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 4526–4550 of 7282 papers

TitleStatusHype
Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applications—0
Undecimated Wavelet Transform for Word Embedded Semantic Marginal Autoencoder in Security improvement and Denoising different Languages—0
Least Squares Regression Can Exhibit Under-Parameterized Double Descent—0
Plug-And-Play Learned Gaussian-mixture Approximate Message Passing—0
Plug and play methods for magnetic resonance imaging (long version)—0
Plug-and-Play Posterior Sampling for Blind Inverse Problems—0
Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation—0
Under-Sampled High-Dimensional Data Recovery via Symbiotic Multi-Prior Tensor Reconstruction—0
Plug-and-Play Unplugged: Optimization Free Reconstruction using Consensus Equilibrium—0
Pluralistic Aging Diffusion Autoencoder—0
Understanding Representation Dynamics of Diffusion Models via Low-Dimensional Modeling—0
Understanding Symmetric Smoothing Filters: A Gaussian Mixture Model Perspective—0
PNEN: Pyramid Non-Local Enhanced Networks—0
Unfolded proximal neural networks for robust image Gaussian denoising—0
A CT Image Denoising Method Based on Projection Domain Feature—0
Underwater Acoustic Signal Denoising Algorithms: A Survey of the State-of-the-art—0
Point Cloud Denoising and Outlier Detection with Local Geometric Structure by Dynamic Graph CNN—0
Point cloud denoising based on tensor Tucker decomposition—0
Underwater Color Restoration Using U-Net Denoising Autoencoder—0
Point Cloud Denoising via Moving RPCA: MRPCA—0
Point Cloud Denoising With Fine-Granularity Dynamic Graph Convolutional Networks—0
Point Cloud Resampling with Learnable Heat Diffusion—0
Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks—0
PointDiffuse: A Dual-Conditional Diffusion Model for Enhanced Point Cloud Semantic Segmentation—0
Point Processes and spatial statistics in time-frequency analysis—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