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Deblurring Medical Images Using a New Grünwald-Letnikov Fractional Mask
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DOI：10.15388/24-INFOR573.png)
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Abstract
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In this paper, we propose a novel image deblurring approach that utilizes a new mask based on the Gr & uuml;nwald-Letnikov fractional derivative. We employ the first five terms of the Gr & uuml;nwald-Letnikov fractional derivative to construct three masks corresponding to the horizontal, vertical, and diagonal directions. Using these matrices, we generate eight additional matrices of size 5 x 5 for eight different orientations: k4 pi, where k = 0, 1, 2, ... , 7. By combining these eight matrices, we construct a 9 x 9 mask for image deblurring that relates to the order of the fractional derivative. We then categorize images into three distinct regions: smooth areas, textured regions, and edges, utilizing the Wakeby distribution for segmentation. Next, we determine an optimal fractional derivative value tailored to each image category to effectively construct masks for image deblurring. We applied the constructed mask to deblur eight brain images affected by blur. The effectiveness of our approach is demonstrated through evaluations using several metrics, including PSNR, AMBE, and Entropy. By comparing our results to those of other methods, we highlight the efficiency of our technique in image restoration.
Keywords:
Gr & uuml
nwald-Letnikov fractional derivatives
gradient matrix
Wakeby distribution.
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