基于稀疏带状矩阵的二维图像重建

摘要

Compressed Sensing(CS)is a new technique for simultaneous data sampling and compression. In this paper, aiming at the dimension of measurement matrix is too high for large images to storage, sparse-banded matrix is introduced for recon-structing signals. It reduces independent random variables of measurement matrix. Through processing the large images column by column, the dimension of measurement matrix is greatly reduced. The experimental results show that the image reconstruction algorithm, based on sparse-banded measurement matrix and the approach of column by column, ensures reconstruction quality of images and lower the reconstruction speed.%　　压缩传感（Compressed Sensing，CS）是数据采样同时实现压缩的新理论、新技术。针对大图像重构时采用的测量矩阵维数高，所需存储空间过大的问题，引入稀疏带状概念，提出了稀疏带状测量矩阵，可减少测量矩阵独立随机元，根据图像按列逐步处理的方式，测量矩阵维数大大降低。实验结果表明基于稀疏带状测量矩阵的逐列图像重构算法在保证重建质量的情况下，计算速度也大大提升。