Fractional Derivatives Application to Image Fusion Problems

Area of Science:

• Image Processing
• Calculus
• Computer Vision

Background:

• Multispectral image fusion combines information from multiple spectral bands to enhance image quality.
• Fractional calculus offers advanced mathematical tools for signal and image analysis.
• Traditional image processing often relies on integer-order derivatives.

Purpose of the Study:

• To analyze the application of fractional order calculus in multispectral image fusion.
• To investigate the definitions and methods for calculating fractional order derivatives in digital images.
• To evaluate the impact of fractional order on image fusion quality and compare with integer-order methods.

Main Methods:

• Analysis of fractional order calculus definitions relevant to image processing.
• Testing various methods for computing fractional derivatives of digital images.
• Implementing and evaluating multispectral image fusion using fractional derivatives.
• Comparative analysis against fusion methods employing integer order derivatives.

Main Results:

• Demonstration of fractional order derivative methods for image fusion.
• Quantification of the influence of fractional order on fusion outcomes.
• Comparative performance assessment highlighting advantages of fractional methods.

Conclusions:

• Fractional order calculus provides a viable and effective approach for multispectral image fusion.
• The choice of fractional order significantly impacts the quality of the fused image.
• Fractional derivative-based fusion methods show potential for superior results compared to integer-order methods.