Gray level image enhancement using nature inspired optimization algorithm: An objective based approach

Image enhancement plays a crucial role in almost every image processing system. The main aim of the image enhancement is to improve image quality by maximizing the information content in the given input image. Histogram Equalization (HE) and Adaptive Histogram Equalization (AHE) are most popular non-heuristic or classical techniques for image enhancement by preserving main features of the input image. These techniques are failed in offering good enhancement. Histogram equalization is an algorithmically complex task and also exhaustive approach. So, artificial intelligence techniques have been proposed for image enhancement problem. The quality of the input image is improved by selecting the optimal parameters based on objective function during optimization process. So the objective function plays an important role in optimization problem. In this context, this paper presents an efficient objective approach for gray level image enhancement using novel optimization algorithm State of Matter Search (SMS). The proposed approach has been tested on standard test gray level images and the results obtained are compared with existing objective approach/algorithms such as CS (Cuckoo Search), ABC (Artificial Bee Colony), APSO (Adaptive Particle Swam Optimization) and DE (Differential Evolution). The proposed approach/algorithm has proven its superiority. All simulations are self-developed MATLAB scripts using MATLAB R2010a on an Intel Core 2 Duo 2.93 GHz processor with 4 GB RAM.