SHARPENING METHODS FOR LOW-CONTRAST IMAGES BASED ON NONLOCAL DIFFERENCES

Various phenomena, such as quantum noise and scattering, existing in the industrial X-ray imaging process, and structural complexity of the measured workpiece resulted in low-contrast and blurry industrial X-ray images, which caused interference to the X-ray image analysis. This study proposed an improved adaptive sharpening algorithm to enhance the contrast and quality of X-ray images. Image pixels and the neighborhood established a nonlocal feature relationship through nonlocal filtering model on the basis of the structural similarity index measure (SSIM) model. The structural similarity of block-based pixels within the search window area was calculated. The improved weight was combined with image sharpening with high-enhancement results. The edge-preserving ability of the algorithm was verified through image tests. Finally, the proposed algorithm contributed to improving the quality of the contrast of industrial X-ray images through simulation experiments. Results demonstrate that features in the neighborhood based on nonlocal differences reflect rich details of images. The X-ray images sharpened with the proposed algorithm are characterized with excellent visual effects and rich details, with information entropy (IE) values of 2.1464, 4.2453, and 3.7283 and structural similarities of 0.9521, 0.9238, and 0.9534. The weights calculated by SSIM indicate that a high similarity in structure exists between the sharpened image and the original one. Images processed by the sharpening algorithm based on nonlocal differences present prominent details while effectively maintaining the objective parameter values. This study provides references to improve the quality of low-contrast images. Key words: nonlocal, SSIM (structural similarity index measure), sharpening, low contrast