Morphological Component Analysis for Textural Enhancement

In practice, image segmentation can be performed in many areas like medical and satellite communications to detect objects and regions in the image.The texture enhancement methods representing all texture information using a single image component. In previous texture enhancement methods reduce noise or artifacts in the image to highlight the textures with the help of filters which reduces the quality of the image. In this project propose a new texture enhancement method using Morphological Component Analysis which uses image decomposition that allows different visual characteristics of textures to be represented by separate components. This method is intended to be a preprocessing step to the use of texture based segmentation algorithms. It uses the modification of Morphological Component Analysis which allows textures to be separated into multiple components each representing different visual characteristics of texture. It select four such texture characteristics and propose a new dictionaries to extract these components using Morphological Component Analysis (MCA). This method produces superior results compared to comparator methods for all segmentation algorithms tested. It results the clusters of local texture features of each distinct image texture to mutually diverge within the multidimensional feature space to a vastly superior degree competes the comparator enhancement methods. The motivation for this project is to extract the greater performance from any texture based segmentation method by establishing a general purpose texture enhancement algorithm.