Partial MURA dataset for experimental evaluation.

Shoulder X-ray images typically have low contrast and high noise levels, making it challenging to distinguish and identify subtle anatomical structures. While existing image enhancement techniques are effective in improving contrast, they often overlook the enhancement of sharpness, especially when amplifying blurring and noise. These techniques may improve detail contrast but fail to maintain overall image clarity and the distinction between the target and the background. To address these issues, we propose a novel image enhancement method aimed at simultaneously improving both the contrast and sharpness of shoulder X-ray images. The method integrates automatic tissue attenuation techniques, which enhance the image contrast by removing non-essential tissue components while preserving important tissues and bones. Additionally, we apply an improved Type-II fuzzy set algorithm to further optimize image sharpness. By simultaneously enhancing contrast and sharpness, the method significantly improves image quality and detail distinguishability. When tested on certain images from the MURA dataset, the proposed method achieved the best or second-best results, outperforming five no-reference image quality assessment metrics. In comparative studies, the method demonstrated significant performance advantages over 10 contemporary X-ray image enhancement algorithms and was validated through ablation experiments to confirm the effectiveness of each module.