Enhancing Image Quality through Fractional Order Unsharp Masking with Particle Swarm Optimization

This paper presents a novel approach to image enhancement using Fractional-Order Unsharp Masking (FOUM) combined with Particle Swarm Optimization (PSO). The proposed method aims to improve the quality of digital images by enhancing their contrast and details. The PSO is employed to optimize the parameters involved in the FOUM process which includes the fractional order, and the weights for combining Laplacian and fractional differential filter. In this approach, Grunwald-Letnikov fractional differential filter is employed. Experimental results demonstrate that the proposed method outperforms traditional image enhancement techniques in terms of entropy and average values, yielding images with enhanced visual quality and improved feature preservation.