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- W2781702585 abstract "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." @default.
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- W2781702585 date "2019-01-01" @default.
- W2781702585 modified "2023-10-16" @default.
- W2781702585 title "A Convex Approach to Superresolution and Regularization of Lines in Images" @default.
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- W2781702585 doi "https://doi.org/10.1137/18m118116x" @default.
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