  We propose a novel method to accurately reconstruct a set of images
representing a single scene from few linear multi-view measurements. Each
observed image is modeled as the sum of a background image and a foreground
one. The background image is common to all observed images but undergoes
geometric transformations, as the scene is observed from different viewpoints.
In this paper, we assume that these geometric transformations are represented
by a few parameters, e.g., translations, rotations, affine transformations,
etc.. The foreground images differ from one observed image to another, and are
used to model possible occlusions of the scene. The proposed reconstruction
algorithm estimates jointly the images and the transformation parameters from
the available multi-view measurements. The ideal solution of this multi-view
imaging problem minimizes a non-convex functional, and the reconstruction
technique is an alternating descent method built to minimize this functional.
The convergence of the proposed algorithm is studied, and conditions under
which the sequence of estimated images and parameters converges to a critical
point of the non-convex functional are provided. Finally, the efficiency of the
algorithm is demonstrated using numerical simulations for applications such as
compressed sensing or super-resolution.
