Fractional Calculus in Signal Processing: The Use of Fractional Calculus in Signal Processing Applications, Such as Image Denoising, Filtering, and Time Series Analysis

Fractional calculus has transformed signal processing by providing a more flexible representation of complex signals. This article explores its applications in image denoising, filtering, and time series analysis, highlighting potential to revolutionize methodologies. calculus, dealing with derivatives integrals non-integer orders, captures long-memory self-similar properties real-world Image denoising benefits from fractional integrals, preserving essential details while removing noise. Filtering signals allows for dynamic adaptability changing characteristics. Time analysis accurate modelling, enhancing predictive capabilities. The future scope promises further advancements, making it valuable tool researchers practitioners seeking analyze across various domains.