Super-resolution reconstruction of lung medical images integrating biological vision mechanisms

In this paper, a Gabor detail feature-enhanced super-resolution network is constructed concerning the biological vision mechanism to address the problem on loss of texture detail information and contextual information in the process of medical image super-resolution. The network infrastructure is a generative adversarial network consisting of a dense residual network generator and a dual-path U-Net discriminator. The Gabor Detail feature extract Module (GDfeM) is designed to address the problem of texture detail information loss by simulating the working mechanism of simple cells. Then, for the problem of insufficient texture detail generation ability of the generator, one of the convolutional blocks of the dense residual network is replaced by GDfeM; for the problem of insufficient texture detail feature extraction ability of the discriminator, aligning the structure of the main pathway U-Net, several GDfeMs and ordinary convolutional kernels are connected to build a detail feature extraction pathway in series, This pathway is connected in parallel with the main pathway through skip connections to form a dual-pathway discriminator. Meanwhile, to address the problem of missing contextual information in the process of super-resolution reconstruction of medical images, a context loss function is introduced to make the network focus on the contextual structural information of the image to reduce distortion and artifacts. Experiments show that our method has $\mathrm{PSNR}$ values of 36.562 dB and 35.560 dB, $\mathrm{SSIM}$ values of 0.921 and 0.903, and PI values of 3.820 and 2.552 for the 4× super-resolution task on the ChestX-ray8 dataset and the Kaggle Comprehensive CT Scanning Imaging dataset, respectively, and that the $\mathrm{PSNR}$ and $\mathrm{SSIM}$ values are not the best when compared to other methods, but the obtained PI values are the lowest among all the methods, which shows that our method plays an important role in helping to obtain high-resolution images with more complete, realistic and accurate texture details.