事务(进程 ID 96)与另一个进程被死锁在 锁 资源上,并且已被选作死锁牺牲品。请重新运行该事务。 基于多尺度特征融合的电子支气管镜暗区无监督视觉增强算法-《中国医学物理学杂志》

[1]彭真,张玲,魏雪梅,等.基于多尺度特征融合的电子支气管镜暗区无监督视觉增强算法[J].中国医学物理学杂志,2026,43(6):803-810.[doi:DOI:10.3969/j.issn.1005-202X.2026.06.014]
 PENG Zhen,ZHANG Ling,WEI Xuemei,et al.Unsupervised visual enhancement for dark regions in electronic bronchoscopic images based on multi-scale feature fusion[J].Chinese Journal of Medical Physics,2026,43(6):803-810.[doi:DOI:10.3969/j.issn.1005-202X.2026.06.014]
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基于多尺度特征融合的电子支气管镜暗区无监督视觉增强算法()

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
43卷
期数:
2026年第6期
页码:
803-810
栏目:
医学影像物理
出版日期:
2026-06-26

文章信息/Info

Title:
Unsupervised visual enhancement for dark regions in electronic bronchoscopic images based on multi-scale feature fusion
文章编号:
1005-202X(2026)06-0803-08
作者:
彭真1张玲1魏雪梅1吴建兴2冉腾2
1.新疆维吾尔自治区人民医院呼吸与危重症医学中心, 新疆 乌鲁木齐 830001; 2.新疆大学机械工程学院、智能制造现代产业学院, 新疆 乌鲁木齐 830017
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
关键词:
深度学习离散小波变换电子支气管镜视觉增强
Keywords:
Keywords: deep learning discrete wavelet transform electronic bronchoscope visual?nhancement
分类号:
R318;TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2026.06.014
文献标志码:
A
摘要:
目的:探讨基于深度学习的视觉增强网络在电子支气管镜图像检测价值。方法:纳入600例术后检测患者基于支气管镜检测图像制作数据集,向多尺度视觉增强网络中分别引用基于Haar离散小波变换下采样、Vision?ransformer、空间残差增强模块和高低频特征合融合模块来增强网络对图像中暗区域的恢复。与EnGAN和URetinex模型进行定性定量对比实验。结果:本文模型可以有效恢复图像暗区域。其在数据集中NIQE和BTMQI指标中效果最优,相比于原图NIQE降低13.01%,BTMQI提升11.62%。BRISQUE指标效果也极其优秀,相比于原图降低9.15%。通过定性对比实验,本文模型可视化效果强于其他模型。结论:本文模型可提升电子支气管镜图像暗区域的恢复效果。
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.

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备注/Memo

备注/Memo:
【收稿日期】2026-01-10 【基金项目】新疆维吾尔自治区自然科学基金青年科学基金(2024D01C291) 【作者简介】彭真,硕士,主治医师,研究方向:图像处理,E-mail: 673078178@qq.com 【通信作者】魏雪梅,博士,主任医师,研究方向:图像处理,E-mail: weixuemei@163.com
更新日期/Last Update: 2026-06-29