This article explores the application of fractional calculus in image processing, focusing on image enhancement and denoising. Fractional calculus extends traditional integer-based calculus to non-integer orders, offering powerful tools for engineering applications. The study examines the effectiveness of fractional differential and integral operators in enhancing image features and reducing noise. Experimental results show that fractional-order differential operators outperform integer-order counterparts in accentuating weak edges and strong textures, while fractional integral operators excel in denoising by improving the signal-to-noise ratio and preserving essential features like edges and textures. The research also investigates the amplitude-frequency characteristics of fractional calculus operators, revealing their unique properties in signal processing. Fractional-order differential operators act as high-pass filters, enhancing high-frequency components while preserving low-frequency details. Fractional-order integral operators function as low-pass filters, attenuating high-frequency noise while maintaining low-frequency information. The study presents image enhancement and denoising experiments using fractional-order differential and integral operators, demonstrating their superior performance compared to traditional methods. The results show that fractional calculus-based image processing techniques yield better contrast, clarity, and noise reduction, particularly in low-level image processing. The study concludes that fractional calculus offers significant advantages in image processing, with potential for further innovation in integrating fractional-order calculus with intelligent algorithms. Future research may explore adaptive fractional-order image enhancement and denoising, as well as the use of distributed orders or nonsingular kernels to improve results. The findings highlight the practical utility of fractional calculus in image processing, paving the way for advanced applications in various fields.This article explores the application of fractional calculus in image processing, focusing on image enhancement and denoising. Fractional calculus extends traditional integer-based calculus to non-integer orders, offering powerful tools for engineering applications. The study examines the effectiveness of fractional differential and integral operators in enhancing image features and reducing noise. Experimental results show that fractional-order differential operators outperform integer-order counterparts in accentuating weak edges and strong textures, while fractional integral operators excel in denoising by improving the signal-to-noise ratio and preserving essential features like edges and textures. The research also investigates the amplitude-frequency characteristics of fractional calculus operators, revealing their unique properties in signal processing. Fractional-order differential operators act as high-pass filters, enhancing high-frequency components while preserving low-frequency details. Fractional-order integral operators function as low-pass filters, attenuating high-frequency noise while maintaining low-frequency information. The study presents image enhancement and denoising experiments using fractional-order differential and integral operators, demonstrating their superior performance compared to traditional methods. The results show that fractional calculus-based image processing techniques yield better contrast, clarity, and noise reduction, particularly in low-level image processing. The study concludes that fractional calculus offers significant advantages in image processing, with potential for further innovation in integrating fractional-order calculus with intelligent algorithms. Future research may explore adaptive fractional-order image enhancement and denoising, as well as the use of distributed orders or nonsingular kernels to improve results. The findings highlight the practical utility of fractional calculus in image processing, paving the way for advanced applications in various fields.