Fractional Calculus In Image Processing: A Review
It summarizes existing methods for researchers in image processing, but is incremental as it does not introduce new techniques.
This paper reviews the application of fractional calculus in image processing, highlighting that fractional-order derivatives provide an extra free parameter for optimization and have been successfully used across ten sub-fields such as image enhancement and denoising.
Over the last decade, it has been demonstrated that many systems in science and engineering can be modeled more accurately by fractional-order than integer-order derivatives, and many methods are developed to solve the problem of fractional systems. Due to the extra free parameter order, fractional-order based methods provide additional degree of freedom in optimization performance. Not surprisingly, many fractional-order based methods have been used in image processing field. Herein recent studies are reviewed in ten sub-fields, which include image enhancement, image denoising, image edge detection, image segmentation, image registration, image recognition, image fusion, image encryption, image compression and image restoration. In sum, it is well proved that as a fundamental mathematic tool, fractional-order derivative shows great success in image processing.