Deep Learning-Based Denoising in High-Speed Portable Reflectance Confocal Microscopy.

ABSTRACT:

Background and objective

Portable confocal microscopy (PCM) is a low-cost reflectance confocal microscopy technique that can visualize cellular details of human skin in vivo. When PCM images are acquired with a short exposure time to reduce motion blur and enable real-time 3D imaging, the signal-to-noise ratio (SNR) is decreased significantly, which poses challenges in reliably analyzing cellular features. In this paper, we evaluated deep learning (DL)-based approach for reducing noise in PCM images acquired with a short exposure time.

Study design/materials and methods

Content-aware image restoration (CARE) network was trained with pairs of low-SNR input and high-SNR ground truth PCM images obtained from 309 distinctive regions of interest (ROIs). Low-SNR input images were

SUBMITTER: Zhao J 

PROVIDER: S-EPMC8273118 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature