期刊
SIAM JOURNAL ON IMAGING SCIENCES
卷 12, 期 1, 页码 211-258出版社
SIAM PUBLICATIONS
DOI: 10.1137/18M118116X
关键词
superresolution; sparse recovery; convex optimization; line detection; splitting method; spectral estimation
We present a new convex formulation for the problem of recovering lines in degraded images. Following the recent paradigm of superresolution, we formulate a dedicated atomic norm penalty and we solve this optimization problem by means of a primal-dual algorithm. This parsimonious model enables the reconstruction of lines from lowpass measurements, even in presence of a large amount of noise or blur. Furthermore, a Prony method performed on rows and columns of the restored image, provides a spectral estimation of the line parameters, with subpixel accuracy.
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