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 4451–4475 of 7282 papers

TitleStatusHype
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET DenoisingCode0
Variational Denoising for Variational Quantum Eigensolver—0
AMC-Net: An Effective Network for Automatic Modulation Classification—0
Exploiting Multilingualism in Low-resource Neural Machine Translation via Adversarial Learning—0
HDR Imaging with Spatially Varying Signal-to-Noise Ratios—0
Steered Mixture of Experts Regression for Image Denoising with Multi-Model-Inference—0
Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration—0
The G-invariant graph Laplacian—0
Robust Andrew's sine estimate adaptive filtering—0
Diffusion Schrödinger Bridge Matching—0
Channel Phase Processing in Wireless Networks for Human Activity Recognition—0
Exploring Efficient Asymmetric Blind-Spots for Self-Supervised Denoising in Real-World Scenarios—0
Whole-body PET image denoising for reduced acquisition time—0
Denoising Autoencoder-based Defensive Distillation as an Adversarial Robustness Algorithm—0
DDMM-Synth: A Denoising Diffusion Model for Cross-modal Medical Image Synthesis with Sparse-view Measurement Embedding—0
GeoTMI:Predicting quantum chemical property with easy-to-obtain geometry via positional denoising—0
Generalizable Denoising of Microscopy Images using Generative Adversarial Networks and Contrastive Learning—0
Pushing The Limits of the Wiener Filter in Image DenoisingCode0
Exploring Continual Learning of Diffusion Models—0
DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-ConsistencyCode0
Shot Noise Reduction in Radiographic and Tomographic Multi-Channel Imaging with Self-Supervised Deep Learning—0
DiffuScene: Denoising Diffusion Models for Generative Indoor Scene SynthesisCode0
Inheriting Bayer's Legacy-Joint Remosaicing and Denoising for Quad Bayer Image Sensor—0
Controllable Inversion of Black-Box Face Recognition Models via Diffusion—0
Parallel Diffusion Model-based Sparse-view Cone-beam Breast CT—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