Research on influence model of fractional calculus derivative Fourier transform on aerobics view image reconstruction

Abstract.Aiming at the shortcomings of the current fractional-calculus Fourier transform applied to image processing, it is necessary to artificially specify the differential order. This paper proposes an adaptive fractional differential, which can be applied to the aerobics view with higher real-time requirements and image reconstruction occasions. The adaptive fractional differential derivative Fourier transform selection can represent the fractal dimension of texture detail complexity as a parameter adaptive method to determine the order of the differential, but the commonly used fractional box dimension calculation method has relatively rough results. Its algorithmic shortcomings and an improved algorithm are proposed to make the fractal dimension obtained by the improved algorithm more accurate. An image reconstruction application based on the improved adaptive-tensor small-frame sparse-regularization algorithm was developed, which verified that feature matching can be performed with corner points. Experiments show that the improved fractal-based adaptive fractional-order differential Fourier transform constructed in this paper has better experimental results than the integer-order differential in edge detection and reconstruction of aerobics view images.

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