Adaptive Fractional-order Differentiation for Enhanced Image Contrast Utilizing Caputo Masks

Image enhancement remains a cornerstone in digital image processing, aiming to improve visual clarity through various methods. Spatial domain techniques include integer-order and fractional-order differentiation. Although widely used, traditional integer-order differentiation techniques suffer from limitations such as indiscriminate spatial frequency treatment and noise amplification, leading to degraded image quality. This paper proposes an adaptive fractional-order differentiation approach employing Caputo fractional differential masks to selectively enhance image details. This approach uses image gradient information to determine the appropriate fractional order. By dynamically adjusting the fractional order based on specific image requirements, the method achieves superior contrast improvement while preserving fine details and minimizing noise. Experimental results, evaluated using metrics such as Pratt's Figure of Merit (FOM), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR), demonstrate that this approach outperforms comparable techniques, highlighting its effectiveness in image enhancement.