We introduce a new approach for LDCT image denoising by employing a deep residual block CNN (DRBNet) with residual noise learning as the denoising prior. (2) Hybrid loss function: The DRBNet prior is ...

In PhotoniX, researchers report a self-supervised deep learning method that denoises ... 2025), introduces a self-supervised denoising network—TeD (Temporal-gradient empowered Denoising ...

Abstract: Industrial process data are usually affected by random and gross errors leading to deviation from the true value and violation of process constraints. Traditional data reconciliation methods ...

Methods: This study integrates rainfall, surface displacement, and vertical displacement monitoring data, and proposes an automatic failure mode identification method based on deep convolutional ...

This algorithm combines the deep learning framework with denoising regularization techniques, enabling the reconstruction of accurate radio maps from sparse data, significantly improving the precision ...