A Novel Progressive Image Scanning and Reconstruction Scheme based on Compressed Sensing and Linear Prediction
This work addresses a computational bottleneck for progressive image acquisition systems, offering an incremental improvement over existing methods.
The paper tackles the computational intractability of reconstructing high-resolution images from compressed sensing measurements in progressive line-by-line acquisition systems, such as flatbed scanners and remote sensing imagers, by developing an iterative algorithm that uses linear prediction to correlate adjacent rows, resulting in very good reconstruction quality with reasonable complexity.
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.