Super-resolution reconstruction of lung ultrasound images based on texture feature enhanced generative adversarial networks (TFEGAN)

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.