论文标题
块分布式分布式将内存梯度算法及其应用于3D图像恢复的应用
Block Distributed Majorize-Minimize Memory Gradient Algorithm and its application to 3D image restoration
论文作者
论文摘要
现代3D图像恢复问题需要强大的优化框架来处理高维度，同时在合理的时间内提供可靠的数值解决方案。从这个角度来看，异步并行优化算法通过克服记忆限制问题和通信瓶颈引起了人们的关注。在这项工作中，我们提出了一个分布式分布式的块，以最小化内存梯度（BD3MG）优化算法，用于解决大型非凸线可区分优化问题。假设分布式存储环境，该算法将有效的3mg方案投入到较小的维度子问题中，其中变量以异步方式解决。通过提出的BD3MG方法构建的序列的收敛是在轻度假设下建立的。与几个同步和异步竞争者相比，通过深度变化的模糊降解的3D图像的恢复表明，我们的方法会产生显着的计算时间缩短，同时表现出巨大的可伸缩性潜力。
Modern 3D image recovery problems require powerful optimization frameworks to handle high dimensionality while providing reliable numerical solutions in a reasonable time. In this perspective, asynchronous parallel optimization algorithms have received an increasing attention by overcoming memory limitation issues and communication bottlenecks. In this work, we propose a block distributed Majorize-Minorize Memory Gradient (BD3MG) optimization algorithm for solving large scale non-convex differentiable optimization problems. Assuming a distributed memory environment, the algorithm casts the efficient 3MG scheme into smaller dimension subproblems where blocks of variables are addressed in an asynchronous manner. Convergence of the sequence built by the proposed BD3MG method is established under mild assumptions. Application to the restoration of 3D images degraded by a depth-variant blur shows that our method yields significant computational time reduction compared to several synchronous and asynchronous competitors, while exhibiting great scalability potential.
