基于深度学习的二值测量矩阵自适应构建方法

Adaptive Construction Method for Binary Measurement Matrix Based on Deep Learning

在基于压缩感知的计算鬼成像领域中,测量矩阵的设计问题一直是被研究的对象。理想的测量矩阵需要满足较高的采样效率、较好的重构效果和较低的硬件实现要求。为了减轻测量矩阵的设计与实现难度,提出了一种基于深度学习的二值测量矩阵的构建方法。该方法通过卷积操作模拟图像的压缩采样过程,并利用设计的采样网络对图像数据进行训练,以自适应的方式对测量矩阵进行迭代更新。仿真与实验结果表明,构建的测量矩阵能够在较低采样率条件下得到高质量的重构图像,进一步促进了计算鬼成像的实际应用。

In the field of computational ghost imaging based on compressed sensing,the design of the measurement matrix has always been a subject of research.The ideal measurement matrix must possess high sampling efficiency,good reconstruction effect,and low hardware-implementation difficulty.To reduce the difficulty of designing and implementing the measurement matrix,this paper proposes a method for constructing a binary measurement matrix based on deep learning.This method uses convolution to simulate the compressed sampling process of the image and trains the image data through the designed sampling network to adaptively and iteratively update the measurement matrix.The results of the simulation and experiments show that the constructed measurement matrix can obtain high-quality reconstructed images under low sampling rate,which further facilitate the practical application of computational ghost imaging.