  Compressed sensing (CS) is an innovative technique allowing to represent
signals through a small number of their linear projections. In this paper we
address the application of CS to the scenario of progressive acquisition of 2D
visual signals in a line-by-line fashion. This is an important setting which
encompasses diverse systems such as flatbed scanners and remote sensing
imagers. The use of CS in such setting raises the problem of reconstructing a
very high number of samples, as are contained in an image, from their linear
projections. Conventional reconstruction algorithms, whose complexity is cubic
in the number of samples, are computationally intractable. In this paper we
develop an iterative reconstruction algorithm that reconstructs an image by
iteratively estimating a row, and correlating adjacent rows by means of linear
prediction. We develop suitable predictors and test the proposed algorithm in
the context of flatbed scanners and remote sensing imaging systems. We show
that this approach can significantly improve the results of separate
reconstruction of each row, providing very good reconstruction quality with
reasonable complexity.
