@article{ZHANG2025, 
author = {Ying ZHANG and Rui HE and EnGuang SUI and Zhe SUN and HuiLin LI and Bo ZHANG and JianLin WANG},
title = {Super-resolution reconstruction of lung ultrasound images based on texture feature enhanced generative adversarial networks (TFEGAN)},
year = {2025},
journal = {Journal of Beijing University of Chemical Technology (Natural Science Edition)},
volume = {52},
number = {6},
pages = {38-48},
keywords = {image super-resolution reconstruction, texture feature enhancement, generative adversarial network, adaptive loss, lung ultrasound imaging},
url = {https://www.sciopen.com/article/10.13543/j.bhxbzr.2025.06.005},
doi = {10.13543/j.bhxbzr.2025.06.005},
abstract = {Super-resolution reconstruction of ultrasound images can effectively improve image quality by enhancing the high-frequency information and enriching the detailed features of the images. To address the problem that existing super-resolution reconstruction of ultrasound images is prone to detail distortion in this work, a method for super-resolution reconstruction of lung ultrasound images based on texture feature enhanced generative adversarial networks (TFEGAN) is proposed. This method employs a multi-scale texture feature extraction module that fully extracts texture information at multiple scales form lung ultrasound images using a multi-branch architecutre. It employs channel attention mechanisms and multi-head self-attention mechanisms to enhance the feature extraction of the super-resolution generative adversarial network. As a result the network is able to dynamically adjust the weights of different channel features and capture long-range dependencies, thereby enhancing its global feature representation capabilities. Finally, by combining a joint loss function with an adaptive loss-weight adjustment strategy, super-resolution reconstruction of lung ultrasound images was achieved. Experimental results show that compared to conventional algorithms such as super-resolution generative adversarial network (SRGAN), enhanced super-resolution generative adversarial networks (ESRGAN), structure-preserving super resolution (SPSR) and content-aware local GAN (CAL-GAN), the learned perceptual image patch similarity (LPIPS) index of our new method is improved by 17.3%, 3.76%, 9.70%, and 2.85%. The reconstructed image texture details are discernible, and the overall image quality is enhanced.}
}