CAJ | 학술논문

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.
압축전감(Compressed Sensing,CS)시수거채양동시실현압축적신이론、신기술.침대대도상중구시채용적측량구진유수고,소수존저공간과대적문제,인입희소대상개념,제출료희소대상측량구진,가감소측량구진독립수궤원,근거도상안렬축보처리적방식,측량구진유수대대강저.실험결과표명기우희소대상측량구진적축렬도상중구산법재보증중건질량적정황하,계산속도야대대제승.
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.