CoShaRP: a convex program for single-shot tomographic shape sensing

We introduce single-shot X-ray tomography that aims to estimate the target image from a single cone-beam projection measurement. This linear inverse problem is extremely under-determined since measurements are far fewer than number of unknowns. Moreover, it more challenging conventional where sufficiently large angles forms measurements, allowing for simple inversion process. However, becomes less severe if only composed known shapes. Hence, shape prior transforms ill-posed estimation non-linear estimating roto-translations In this paper, we circumvent non-linearity by using dictionary possible propose convex program CoShaRP recover dictionary-coefficients successfully. relies on simplex-type constraint and can be solved quickly primal-dual algorithm. The numerical experiments show recovers shapes stably moderately noisy measurements.