Abstract

Info

Title:: Unsupervised visual enhancement for dark regions in electronic bronchoscopic images based on multi-scale feature fusion

Author(s):: PENG Zhen1; ZHANG Ling1; WEI Xuemei1; WU Jianxing2; RAN Teng2; 1. Respiratory and Critical Care Medicine Center, Peoples Hospital of Xinjiang Uygur Autonomous Region, Urumqi 830001, China 2. School of Intelligent Manufacturing and Modern Industry (School of Mechanical Engineering), Xinjiang University, Urumqi 830017, China

Keywords:: Keywords: deep learning discrete wavelet transform electronic bronchoscope visual?nhancement

Abstract:: Abstract: Objective To explore the potential of a deep learning-based visual enhancement network in the analysis of electronic bronchoscopic images. Methods A dataset was constructed using bronchoscopic images acquired from 600 postoperative patients. A multi-scale visual enhancement network was developed to improve the restoration of dark regions through Haar discrete wavelet transform-based downsampling, Vision?ransformer, spatial residual enhancement module, and high- and low-frequency feature fusion module. Qualitative and quantitative comparisons with EnGAN and URetinex models were performed. Results The proposed model effectively restored dark regions in bronchoscopic images, and achieved optimal NIQE and BTMQI on the dataset, with a 13.01% reduction in NIQE and an 11.62% increase in BTMQI compared with the original images. Additionally, it obtained a favorable BRISQUE value which was decreased by 9.15% relative to the original images. Qualitative comparisons further demonstrated its superior visual performance over other models. Conclusion The proposed model can improve the restoration of dark regions in electronic bronchoscopic images.