The maximum subarray problem is used to identify the subarray of a two-dimensional array, where the sum of elements is maximized. In terms of image processing, the solution has been used to find the brightest region within an image. Two parallel algorithms of the maximum subarray problem solve this problem in O(n) and O(log n) time. A field programmable gate array implementation has verified theoretical maximum performance; however, extensive customization is required, restricting general application. A more convenient platform for this work is a graphics processor unit since it offers a flexible trade-off between hardware customization and performance. Implementation of the maximum subarray algorithm on a graphics processor unit is discussed in this article for rectangular solutions and convex extensions are explored.
Proc. SPIE. 8500, Image Reconstruction from Incomplete Data VII
KEYWORDS: Clocks, Radio optics, Image processing, Wavefront sensors, Field programmable gate arrays, Radio telescopes, Very large scale integration, Astronomical imaging, Algorithm development, Radio astronomy
The maximum sub-array algorithm has been implemented within a field programmable gate array as an efficient centroiding method for wavefront slope estimation. However, a convenient platform for this work is a graphics processor unit (GPU). Translation of the maximum subarray algorithm to a GPU has been performed and shows significant performance gains compared to a single-core CPU. Recently, this algorithm has been applied to radio telescope images acquired for the Australian square kilometer array pathfinder project. This paper provides an overview of the maximum subarray algorithm and shows how this can be utilized for optical and radio telescope applications.