Construction of an edge detection mask based on Caputo fractional derivative
DOI:
https://doi.org/10.22399/ijcesen.2671Keywords:
Fractional derivative, Image edge detection, Caputo fractional derivative, Convolution mask, Peak Signal to Noise Ratio (PSNR)Abstract
Fractional calculus has recently attracted significant attention, making substantial contributions to the field of digital image processing. This growing interest stems from its ability to offer a more nuanced mathematical framework for analyzing and processing images. In this paper, we present an innovative approach to edge detection that leverages the Caputo fractional derivative. Unlike traditional edge detection methods, which may overlook subtle variations in pixel intensity, our approach utilizes the Caputo definition to enhance the edge identification process, thereby capturing finer details in the image. By applying this fractional derivative, we achieve a more precise and detailed representation of edges, which is particularly useful in scenarios requiring high accuracy. The effectiveness of our method is quantitatively assessed using the Peak Signal to Noise Ratio (PSNR), that measures the quality of the processed image relative to the original. The results indicate that the Caputo fractional derivative not only effectively highlights edges but also maintains an optimal balance between preserving intricate details and minimizing noise, making it a powerful tool in digital image processing.
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