Mask Pyramid Methodology For Enhanced Localization In Image Fusion And Enhancement

Image fusion is a process that combines regions of images from different sources into a single fused image based on a salience selection rule for each region. In this paper, we proposed an algorithmic approach using a mask pyramid to better localize the selection process. A mask pyramid operates in different scales of the image to improve the fused image quality beyond a global selection rule. The proposed approach offers a generic methodology for applications in image enhancement, high dynamic range compression, depth of field extension, and image blending. The mask pyramid can also be encoded for intelligent analysis of source imagery. Several examples of this mask pyramid method are provided to demonstrate its performance in a variety of applications. A new embedded system architecture that builds upon the Acadia (R) II Vision Processor is proposed.