Adaptive compressed sampling based on extended wavelet trees

作者：Dai, Huidong; Gu, Guohua^*; He, Weiji; Liao, Fajian; Zhuang, Jiayan; Liu, Xingjiong; Chen, Qian

来源：Applied Optics, 2014, 53(29): 6619-6628.

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.

• 出版日期2014-10-10
• 单位南京理工大学