  X-ray imaging is a fundamental clinical tool for screening and diagnosing
various diseases. However, the spatial resolution of radiographs is often
limited, making it challenging to diagnose small image details and leading to
difficulties in identifying vertebrae anomalies at an early stage in chest
radiographs. To address this limitation, we propose a novel and robust approach
to significantly improve the quality of X-ray images by iteratively training a
deep neural network. Our framework includes an embedded internal guidance layer
that enhances the fine structures of spinal vertebrae in chest X-ray images
through fully unsupervised training, utilizing an iterative procedure that
employs the same network architecture in each enhancement phase. Additionally,
we have designed an optimized loss function that accurately identifies object
boundaries and enhances spinal features, thereby further enhancing the quality
of the images. Experimental results demonstrate that our proposed method
surpasses existing detail enhancement methods in terms of BRISQUE scores, and
is comparable in terms of LPC-SI. Furthermore, our approach exhibits superior
performance in restoring hidden fine structures, as evidenced by our
qualitative results. This innovative approach has the potential to
significantly enhance the diagnostic accuracy and early detection of diseases,
making it a promising advancement in X-ray imaging technology.
