Medical Image Enhancement using Guided Filtering and Chaotic Inertia Weight Black Hole Algorithm

In this study, a new hybrid approach is suggested for medical image enhancement. The main idea is based on the hybrid of the guided filter and chaotic inertia weight black hole algorithm (GFCBH) to enhance and highlight the image information using a new objective function. GFCBH is a two-stage approach that, first, applies the guided filter to the input image which performs as an edge-preserving smoothing operator, and then, uses the CBH algorithm to automatically find optimal parameters for transformation function based on the objective function. In the proposed objective function, universal image quality index (Q), entropy, edge pixels, and gray level cooccurrence matrix (GLCM) based contrast and energy are considered to achieve the best-enhanced image. The experimental results are verified by comparison with ten well-known image enhancement techniques using entropy and peak signal-to-noise-ratio (PSNR) measurement criteria. The extensive experiments along with qualitative and quantitative evaluations show that the suggested method can successfully enhance images and performs better than most state-of-art techniques.