Compressed sensing (CS) is an innovative technique allowing to representsignals through a small number of their linear projections. In this paper weaddress the application of CS to the scenario of progressive acquisition of 2Dvisual signals in a line-by-line fashion. This is an important setting whichencompasses diverse systems such as flatbed scanners and remote sensingimagers. The use of CS in such setting raises the problem of reconstructing avery high number of samples, as are contained in an image, from their linearprojections. Conventional reconstruction algorithms, whose complexity is cubicin the number of samples, are computationally intractable. In this paper wedevelop an iterative reconstruction algorithm that reconstructs an image byiteratively estimating a row, and correlating adjacent rows by means of linearprediction. We develop suitable predictors and test the proposed algorithm inthe context of flatbed scanners and remote sensing imaging systems. We showthat this approach can significantly improve the results of separatereconstruction of each row, providing very good reconstruction quality withreasonable complexity.
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