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 5076–5100 of 7282 papers

TitleStatusHype
When A Conventional Filter Meets Deep Learning: Basis Composition Learning on Image FiltersCode0
Image reconstruction algorithms in radio interferometry: from handcrafted to learned regularization denoisers—0
PRNU Emphasis: a Generalization of the Multiplicative Model—0
Does prior knowledge in the form of multiple low-dose PET images (at different dose levels) improve standard-dose PET prediction?—0
The PCG-AIID System for L3DAS22 Challenge: MIMO and MISO convolutional recurrent Network for Multi Channel Speech Enhancement and Speech Recognition—0
Multi-Channel Speech Denoising for Machine Ears—0
Distributed Machine-Learning for Early HARQ Feedback Prediction in Cloud RANs—0
Texture Aware Autoencoder Pre-training And Pairwise Learning Refinement For Improved Iris Recognition—0
Grasp-and-Lift Detection from EEG Signal Using Convolutional Neural Network—0
Temporal evolution of the Covid19 pandemic reproduction number: Estimations from proximal optimization to Monte Carlo sampling—0
Monotonically Convergent Regularization by Denoising—0
A GAN-based Denoising Method for Chinese Stele and Rubbing Calligraphic Image—0
Reducing Redundancy in the Bottleneck Representation of the Autoencoders—0
InferGrad: Improving Diffusion Models for Vocoder by Considering Inference in Training—0
Adaptive Mixing of Auxiliary Losses in Supervised LearningCode0
Supervision by Denoising for Medical Image Segmentation—0
Exploring Self-Attention Mechanisms for Speech Separation—0
Image-to-Image MLP-mixer for Image ReconstructionCode0
Bregman Plug-and-Play Priors—0
SparGE: Sparse Coding-based Patient Similarity Learning via Low-rank Constraints and Graph Embedding—0
Posterior temperature optimized Bayesian models for inverse problems in medical imagingCode0
Error Correction in ASR using Sequence-to-Sequence Models—0
Practical Noise Simulation for RGB Images—0
Low-Rank Tensor Completion Based on Bivariate Equivalent Minimax-Concave Penalty—0
A Priori Denoising Strategies for Sparse Identification of Nonlinear Dynamical Systems: A Comparative Study—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