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.
翻译:线性问题出现在不同的学科中,其用于传输矩阵恢复是生物医学成像中最令人振奋的挑战之一,其知识将任何随机介质转化为光学工具,通过混乱来聚焦或传递图像。在这里,将输入-输出问题转换成统计机械配方,我们调查推论规程如何通过假象最大化来学习传输联结。连接线性回归和热力学让我们提出一个创新框架来寻求散射中点的解决方案。