Application of Metaheuristic Algorithms for optimal Smartphone-photo enhancement L. M. Rasdi Rere, M. Ivan Fanany, A. Murni Faculty of Computer Science Universitas Indonesia Depok – West Java, Indonesia laode.mohammad@ui.ac.id, ivan@cs.ui.ac.id, aniati@cs.ui.ac.id Abstract— Nowadays taking a photo from smartphones is widely popular because of their simplicity and convenience, but the images from them sometimes suffer from illumination and color distortion, so an enhancement of these images are necessary. There are many kind of image enhancement have been proposed. One of them using metaheuristic. In this paper we pursue efficient metaheuristic algorithms for optimal image contrast enhancement for smartphone–photo. Several smartphone-photos and image from benchmark data are used to evaluate the algorithms. The simulation results show metaheuristic methods are a possible efficient scheme to increase the quality of contrasting from smartphone-photo. Keywords—image enhancement; metaheuristic algorithm; smartphone-photo. I. INTRODUCTION One of the important issues in image processing is image enhancement. The aim of this technology is to improve the appearance of an image, including increase the contrast and sharpen the features, which are to improve their visual quality of human eyes [1]. Image enhancement does not increase the intrinsic information in the original image, but it is beneficial to further image application, such as facilitating image segmentation, recognising and interpreting useful information from the image [2]. Many image enhancement methods have been proposed, one of them using metaheuristic methods to improve the appearance of the image, including increase the contrast and sharpen image features. Some papers [3], [4], [5] report that metaheuristic method outperform than classical point operation (linier contrast stretching and histogram equalization). Metaheuristic is an efficient approach for many problems that cannot be solved optimally using deterministic method within a reasonable time limit. Almost all of these methods are nature-inspired, based on some principles from biology, physics or ethology. Another classification of metaheuristic is Single-solution based metaheuristic and Population-based metaheuristic [6]. A number of metaheuristic methods have been used to enhance an image from smartphone in recent years. Jung et al. [7] proposed Evolutionary Computing for image enhancement interface in consideration of the accessibility to the mobile environment and various constraints. Lee and Cho [8] proposed an automatic image enhancement tool for smartphone by using interactive differential evolution (DE). In this paper we use simulated annealing (SA), DE, particle swarm optimization (PSO) and harmony search (HS) to obtain the best optimization for gray-level and color image contrast enhancement. They were chosen to represent all categories in metaheuristic algorithms. SA is representation of single- solution based and physics phenomena. DE represents population-based and biology phenomena; PSO represent ethology and also population-based. The last one is HS, inspired by musical phenomena as well as population based. The remainder of this paper is organized as follows: Section 2 gives explanation image enhancement problem; Section 3 draws optimization using metaheuristic; Section 4 present simulation result; and Section 5 the conclusion of this paper. II. IMAGE ENHANCEMENT Image enhancement methods selectively emphasize certain information in an image to strengthen the usability of the image. This method can be categorized into four classes; (i) contras enhancement, (ii) edge enhancement, (iii) noise reduction, and (iv) edge restorations. In this paper, we focus on contrast enhancement. The techniques usually used for contrast enhancement, fall into two categories, i.e. indirect methods and direct methods of contrast enhancement [2]. In spatial domain for gray-level image, the enhancement uses transformation function which generates a new intensity value for each pixel of M x N original image to generate the enhance image, where M denotes the number of columns and N denotes the number of rows. Thus g(i, j) = T [f(i, j)] (1) where f(i, j) is the gray value of the (i, j) th pixel of the input images and g(i, j) is the gray value of the (i, j) th pixel of the 2014 IEEE 3rd Global Conference on Consumer Electronics (GCCE) 978-1-4799-05145-1/14/$31.00 ©2014 IEEE 542