A Compressive Regularization Imaging Algorithm for Millimeter-Wave SAIR

Yilong ZHANG  Yuehua LI  Guanhua HE  Sheng ZHANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E98-D   No.8   pp.1609-1612
Publication Date: 2015/08/01
Publicized: 2015/05/07
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2014EDL8256
Type of Manuscript: LETTER
Category: Image Processing and Video Processing
millimeter wave,  SAIR,  compressive regularization,  imaging algorithm,  

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Aperture synthesis technology represents an effective approach to millimeter-wave radiometers for high-resolution observations. However, the application of synthetic aperture imaging radiometer (SAIR) is limited by its large number of antennas, receivers and correlators, which may increase noise and cause the image distortion. To solve those problems, this letter proposes a compressive regularization imaging algorithm, called CRIA, to reconstruct images accurately via combining the sparsity and the energy functional of target space. With randomly selected visibility samples, CRIA employs l1 norm to reconstruct the target brightness temperature and l2 norm to estimate the energy functional of it simultaneously. Comparisons with other algorithms show that CRIA provides higher quality target brightness temperature images at a lower data level.