Dai Huidong, Gu Guohua, He Weiji, Liao Fajian, Zhuang Jiayan, Liu Xingjiong, Chen Qian
Appl Opt. 2014 Oct 10;53(29):6619-28. doi: 10.1364/AO.53.006619.
The theory of compressed sensing (CS) indicates that a signal that is sparse or compressible can be recovered from a relatively small number of nonadaptive linear measurements that is far below the Nyquist-Shannon limit. However, CS suffers from a huge stored and computational overhead when dealing with images of high resolution, taking tens of minutes or longer. In this work, we extend the concept of wavelet trees by adding the sibling relationship and propose an imaging strategy named adaptive compressed sampling based on extended wavelet trees (EWT-ACS). Exploiting both parent-children relationship and sibling relationship in extended wavelet trees, EWT-ACS predicts the locations of significant coefficients adaptively and samples the significant coefficients using a binary digital micromirror device directly. The simulation and experimental results reveal that the proposed strategy breaks through the limitation in CS, and the reconstruction time is reduced significantly. Due to its single-pixel detection mechanism, EWT-ACS shows great potential in many imaging applications.
压缩感知（CS）理论表明，稀疏或可压缩的信号可以从远低于奈奎斯特 - 香农极限的相对少量的非自适应线性测量中恢复。然而，在处理高分辨率图像时，CS会产生巨大的存储和计算开销，需要数十分钟甚至更长时间。在这项工作中，我们通过添加兄弟关系扩展了小波树的概念，并提出了一种基于扩展小波树的自适应压缩采样成像策略（EWT - ACS）。EWT - ACS利用扩展小波树中的父子关系和兄弟关系，自适应地预测重要系数的位置，并直接使用二元数字微镜器件对重要系数进行采样。仿真和实验结果表明，所提出的策略突破了CS的限制，重建时间显著减少。由于其单像素检测机制，EWT - ACS在许多成像应用中显示出巨大潜力。
