Image recovery using partitioned-separable paraboloidal surrogate coordinate ascent algorithms
Image recovery using partitioned-separable paraboloidal surrogate coordinate ascent algorithms
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DOI：
10.1109/83.988963
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发表时间：
2002-03-01
影响因子：
10.6
通讯作者：
Fessler, JA
中科院分区：
文献类型：
--
作者：
Sotthivirat, S;Fessler, JA
迭代坐标上升算法已被证明是有用的图像恢复，但不适合并行计算，由于其顺序的性质。本文提出了一种新的快速收敛的并行图像恢复算法，可以适用于一个非常广泛的目标函数类。该方法是基于抛物面代理函数和一种插值技术。抛物面代理简化了优化问题。该技术的思想是将像素划分为可以并行更新的子集，以减少计算时间。为了快速收敛，使用坐标上升算法顺序地更新每个子集内的像素。该算法保证单调增加的目标函数，并内在地适应非负约束。总结了一个全局收敛性证明。仿真结果表明，该算法比迭代坐标上升算法需要更少的收敛时间。在4个并行处理器的情况下，该算法相对于单处理器坐标上升算法的三维（3-D）共焦图像恢复问题的加速比为3.77。
Iterative coordinate ascent algorithms have been shown to be useful for image recovery, but are poorly suited to parallel computing due to their sequential nature. This paper presents a new fast converging parallelizable algorithm for image recovery that can be applied to a very broad class of objective functions. This method is based on paraboloidal surrogate functions and a concavity technique. The paraboloidal surrogates simplify the optimization problem. The idea of the concavity technique is to partition pixels into subsets that can be updated in parallel to reduce the computation time. For fast convergence, pixels within each subset are updated sequentially using a coordinate ascent algorithm. The proposed algorithm is guaranteed to monotonically increase the objective function and intrinsically accommodates nonnegativity constraints. A global convergence proof is summarized. Simulation results show that the proposed algorithm requires less elapsed time for convergence than iterative coordinate ascent algorithms. With four parallel processors, the proposed algorithm yields a speedup factor of 3.77 relative to single processor coordinate ascent algorithms for a three-dimensional (3-D) confocal image restoration problem.