Recent developments in the domain of information technology have made it possible to extract a knowledge of ocean from input images. The knowledge extraction can be performed using a number of operations such as image segmentation. The major objective of image segmentation is to segment focused and non-focused regions from an input image. The field depth of optical lenses is limited. A camera focuses only on those objects which lie in its field depth, rest of the objects are appeared as non-focused or blurry. For image processing, it is a general requirement that an input image must be all in focus image. In almost each domain such as medical imaging, weapon and aircraft detection, digital photography, and agriculture imaging, it is required to have an all-in focused input image. Image fusion is a process which combines two or more input images to create an all in focused complimentary fused image. Image fusion is considered as a challenging task due to irregular boundaries of focused and non-focused regions. In literature, multiple studies have addressed this issue, however they have reported promising results in creating a fully focused fused image. Moreover, they have considered different features to identify focused and non-focused regions from an input image. For better estimation of focused and non-focused regions,an ensemble of multiple features such as shape and texture-based features can be employed. Furthermore, it is required to obtain optimal weights which are to be assigned to each feature for creating a fused image. The focus of this study is to perform a multi-focus image fusion using an ensemble of multiple local features by weight optimization using a genetic algorithm. To perform this experimentation, nine multi-focus image datasets are collected where each dataset indicates an image pair of multi-focused images. The reason of this selection is two-fold, as they are publicly available, and it contain different types of multi-focus images. For reconstruction of a fully focused fused image, an ensemble of different shape and texture-based features such as Sobel Operator, Laplacian Operator and Local Binary Pattern is employed along with optimal weights obtained using a Genetic Algorithm. The experimental results have indicated improvement over previous fusion methods
Multi-focus Image Fusion based on Genetic Algorithm using Local Features
