Compressed sensing image reconstruction based on morphological component analysis

Compressed sensing (CS) is a new area of signal processing for simultaneous signal sampling and compression. Most of existing methods for CS image reconstruction are suitable for piecewise smooth image, but do not behave well on texture-rich natural image. In this paper, a new optimization problem for CS image reconstruction is proposed, in which different regularization terms are introduced for different morphological components of image. Furthermore, an alternating iterative algorithm is presented to solve the relevant optimization problem. Experimental results show that the proposed method can be applied to reconstruct texture-rich images besides piecewise smooth ones, and outperforms the existing methods on preserving detail feature.