Homomorphic filtering for the image enhancement based on fractional-order derivative and genetic algorithm. (March 2023) - Gamini, Sridevi; Kumar, Samayamantula Srinivas
Highlights: We proposed an image enhancement model based on homomorphic filtering using Grunwald-Letnikov, Riemann-Liouville fractional-order derivatives and genetic algorithm. This work uses a fractional-order derivative to enhance the mid- and high-frequencies and preserve the low-frequencies. The genetic algorithm determines the low and high frequency parameters required for the homomorphic filtering and order of the fractional derivative by optimizing the fitness function. The proposed method is applied on standard images, medical images and images with low contrast and non-uniform illumination conditions and compared with the state-of-the-art methods. Abstract: The main aim of image enhancement is to improve the visual quality or appearance of an image. This article presents an image enhancement method based on Grunwald-Letnikov, Riemann-Liouville fractional-order derivatives and genetic algorithm to boost the homomorphic filtering performance. Homomorphic filtering is used to attenuate the contribution made by the illumination and amplify the reflectance components of an image. This work uses a fractional-order derivative to enhance the mid- and high-frequencies and preserve the low-frequencies. The enhancement of the image depends on the parameters required for the homomorphic filter function and fractional-order value, which are not the same for all types of images. Hence, the genetic algorithm is applied, which automatically determines these parameters by optimizing the fitness function. The capability of the proposed approach is evaluated using performance metrics such as information entropy, average gradient, and contrast improvement index on different sizes of images. An average improvement in information entropy of 6.5%, average gradient of 52%, and contrast improvement index of 75%, respectively, are achieved for standard, medical images and images with low contrast and non-uniform illumination conditions. Also, the proposed method outperforms the existing methods by producing a better visual appearance of the image. Graphical abstract: Image, graphical abstract