现代电子技术2026，Vol.49Issue(1)：34-40,7.DOI:10.16652/j.issn.1004-373x.2026.01.006
基于跨尺度特征融合的内窥镜图像增强算法
Endoscopic image enhancement algorithm based on cross-scale feature fusion
摘要
Abstract
The clinical endoscopic image often suffers from low-quality imaging due to uneven supplementary light sources and reflections from human tissue mucus,resulting in poor image quality,for instance,a large quantity of overexposure.However,the current deep learning based image enhancement algorithms have low feature extraction capabilities due to fixed-size feature fusion,which leads to poor enhancement effects.Therefore,an endoscopic image enhancement algorithm based on cross-scale feature fusion is proposed.In the algorithm,a convolution module(CM)is constructed for high-performance feature extraction and a spatial pyramid pooling-fast(SPPF)module is used to realize the pooling operation of feature maps with different scales.Additionally,a cross-scale feature fusion(CFF)module is introduced into different scales of network layers to achieve multi-scale feature fusion and context information propagation,so as to improve image detail capture and image quality.Experimental results show that the proposed algorithm outperforms the existing algorithms in PSNR and SSIM,in which the PSNR is improved by 9.9%,and the SSIM by 15.4%,achieving high-quality endoscopic image enhancement.关键词
内窥镜图像/深度特征融合/CFF/曝光异常/图像增强算法/金字塔池化模块Key words
endoscopic image/deep feature fusion/CFF/exposure anomaly/image enhancement algorithm/pyramid pooling module分类
信息技术与安全科学引用本文复制引用
刘旭阳,蔡芸,蒋林..基于跨尺度特征融合的内窥镜图像增强算法[J].现代电子技术,2026,49(1):34-40,7.基金项目
国家重点研发计划(2019YFB1310000) （2019YFB1310000）
国家自然科学基金项目(51874217) （51874217）
