Image Restoration using BBO and ACO

Digital image processing plays a vital role in the analysis and interpretation of remotely sensed data. Image restorations techniques help in improving the visibility of any portion or feature of the image suppress sing the information in other portions or features. Image restoration improves the perceptibility of objects in the scene by restoration the brightness difference between objects and their backgrounds. Image restoration is typically performed as a contrast stretch followed by a tonal enhancement, although these could both be performed in one step. Applications of the Ant Colony Optimization (ACO) to solve image processing problem with a reference to a new automatic restoration technique based on real-coded particle ant colony is proposed in this paper. The restoration process is a non-linear optimization problem with several constraints. The objective of the proposed ACO is to maximize an objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. In this paper enhancement occurs on the basis for the development of a new field: biogeography-based optimization (BBO). We discuss natural biogeography and its mathematics, and then discuss how it can be used to solve optimization problems. We see that BBO has features in common with other biology-based optimization methods, such as GAs and particle swarm optimization (PSO). This makes BBO applicable to many of the same types of problems that GAs and PSO are used for, namely, high- restoration problems with multiple local optima.