基于非局部相似度约束的多通道复用压缩遥感成像方法

A multi-channel multiplexing compressive remote sensing approach based on non-local similarity constraint

结合压缩感知成像原理和遥感成像系统的物理可实现性,提出了采用掩膜编码的多通道复用压缩成像方法.首先,采用多组随机二值伯努利分布的掩膜为不同光学通道视场进行压缩编码,在单位积分时间内采集重构图像所需的欠采样数据.然后,针对传统的全变分范数最小化的重构方法易受遥感图像局部突出特征干扰的问题,提出了以遥感图像空间域非局部相似度为正则化重构标准的先验约束.实验结果验证了此压缩成像方法的可行性.与传统算法相比,此重构算法能够在保留图像细节的同时实现有效重构.

A multi-channel multiplexing compressive sensing imaging approach based on compressive sensing is proposed for physical realizable remote sensing systems. First,multi-masks coded with random binary Bernoulli matrix are explored for different optical channels,and the undersampled data of an image are collected in an exposure time. Next,non-local similarity of spatial remote sensing images is presented as the regularization term for reconstruction to remove the reconstructed interference caused by local prominent features in remote sensing scene. The experimental results demonstrate the feasibility of this compressive remote sensing imaging. The proposed algorithm can preserve image details and achieve an effective image reconstruction compared with traditional algorithms.