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The compressive sensing (CS) theory shows that real signals can be exactly recovered from very few samplings. Inspired by the CS theory, the interior problem in computed tomography is proved uniquely solvable by minimizing the region-of-interest's total variation if the imaging object is piecewise constant or polynomial. This is called CS-based interior tomography. However, the CS-based algorithms require high computational cost due to their iterative nature. In this paper, a graphicsdoi:10.1109/access.2014.2340372 fatcat:jiisolg4yzdk5jxgdgbs3kegm4