Image data representation for efficient optimization of objective criterion

摘要

Computing architectures to process image data and optimize an objective criterion are identified. One such objective criterion is the energy in the error function. The data is partitioned and the error function is optimized in stages. Each stage consists of identifying an active partition and performing the optimization with the data in this partition. The other partitions of the data are inactive I.e. maintain their current values. The optimization progresses by switching between the currently active partition and the remaining inactive partitions. In this paper, sequential and parallel update procedures within the active partition are presented. These procedures are applied to retrieve image data from linearly degraded samples. In addition, the local gradient of the error functional is estimated from the observed image data using simple linear convolution operations. This optimization process is effective when the dimensions of the data and the number of partitions increase. The purpose of developing such data processing strategies is to emphasize the conservation of resources such as available bandwidth, computations, and storage in present day Web-based technologies and multimedia information transfer.