Enhancement of Textural Differences Based on Morphological Component Analysis - 2015


This paper proposes a replacement texture enhancement method which uses an image decomposition that permits completely different visual characteristics of textures to be represented by separate parts in contrast with previous methods that either enhance texture indirectly or represent all texture data employing a single image part. Our technique is intended for use as a preprocessing step prior to the employment of texture-primarily based image segmentation algorithms. Our method uses a modification of morphological element analysis (MCA) that permits texture to be separated into multiple morphological components each representing a different visual characteristic of texture. We tend to select four such texture characteristics and propose new dictionaries to extract these parts using MCA. We tend to then propose procedures for modifying each texture element and recombining them to provide a texture-enhanced image. We have a tendency to applied our method as a preprocessing step prior to a range of texture-based mostly segmentation strategies and compared the accuracy of the results, finding that our method produced results superior to comparator strategies for all segmentation algorithms tested. We tend to additionally demonstrate by example the most mechanism by that our technique produces superior results, namely that it causes the clusters of local texture options of each distinct image texture to mutually diverge at intervals the multidimensional feature area to a vastly superior degree versus the comparator enhancement strategies.

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