学习直接和逆传输矩阵
Learning Direct and Inverse Transmission Matrices
摘要：
线性问题出现在各种学科和他们的应用传输矩阵恢复是生物医学成像中最具挑战性的挑战之一。它的知识将任何随机介质变成一种光学工具，可以通过无序聚焦或传输图像。在这里，转换成一个统计力学公式的输入输出问题，我们研究如何推理协议可以学习的传输耦合的伪随机最大化。将线性回归与热力学联系起来，我们提出了一个创新的框架来解决散射之谜。
英文摘要：
Linear problems appear in a variety of disciplines and their application for the transmission matrix recovery is one of the most stimulating challenges in biomedical imaging. Its knowledge turns any random media into an optical tool that can focus or transmit an image through disorder. Here, converting an input-output problem into a statistical mechanical formulation, we investigate how inference protocols can learn the transmission couplings by pseudolikelihood maximization. Bridging linear regression and thermodynamics let us propose an innovative framework to pursue the solution of the scattering-riddle.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
