A New Class of Edge Filter Based on a Cross-correlation-like Equation Derived from Fractional Calculus Principles

In this paper, we propose a new sliding window edge-oriented filter that computes the output pixels using a cross-correlation-like equation derived from the principles of fractional calculus (FC); thus, we call it the “fractional cross-correlation filter” (FCCF). We assessed the performance of this...

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Bibliographic Details
Published in:Applied sciences 2024-07, Vol.14 (13), p.5428
Main Authors: Gonzalez-Lee, Mario, Vazquez-Leal, Hector, Garcia-Martinez, Jose R., Pale-Ramon, Eli G., Morales-Mendoza, Luis J., Nakano-Miyatake, Mariko, Perez-Meana, Hector
Format: Article
Language:English
Subjects:
Algorithms
Calculus
Construction
Deep learning
edge filter
filtering model
fractional calculus
Machine learning
Medical research
Neural networks
Performance evaluation
Signal processing
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Summary:In this paper, we propose a new sliding window edge-oriented filter that computes the output pixels using a cross-correlation-like equation derived from the principles of fractional calculus (FC); thus, we call it the “fractional cross-correlation filter” (FCCF). We assessed the performance of this filter utilizing exclusively edge-preservation-oriented metrics such as the gradient conduction mean square error (GCMSE), the edge-based structural similarity (EBSSIM), and the multi-scale structural similarity (MS-SSIM); we conducted a statistical assessment of the performance of the filter based on those metrics by using the Berkeley segmentation dataset benchmark as a test corpus. Experimental data reveal that our approach achieves higher performance compared to conventional edge filters for all the metrics considered in this study. This is supported by the statistical analysis we carried out; specifically, the FCCF demonstrates a consistent enhancement in edge detection. We also conducted additional experiments for determining the main filter parameters, which we found to be optimal for a broad spectrum of images. The results underscore the FCCF’s potential to make significant contributions to the advancement of image processing techniques since many practical applications such as medical imaging, image enhancement, and computer vision rely heavily on edge detection filters.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14135428