Developing Riemann–Liouville-Fractional Masks for Image Enhancement.

In this article, we focus on the application of fractional differential calculus in image processing, specifically in the context of image decoding techniques. The article presents different methods of image decoding based on Riemann–Liouville fractional integration and compares their performance with established methods in the denoising of noisy images. Here we create new fractional masks by using the Middle Point approach method, the Grumwald–Letnikov scheme, the Toufik–Riemann scheme, and the Euler method with the help of the Riemann–Liouville fractional integral operator. The results demonstrate that the suggested algorithms outperform the compared techniques. The paper also suggests that the concepts presented can be applied to other definitions of fractional calculus. The future research goal is to determine the optimal fractional order for a given set of algorithms. [ABSTRACT FROM AUTHOR]