Super-resolution image reconstruction using multisensors

Super-resolution image reconstruction refers to obtaining an image at a resolution higher than that of a camera (sensor) used in recording the image. In this paper, we present a new joint minimization model in which an objective function is set up consisting of three terms: the data fitting term, the regularization terms for the reconstructed image and the observed low-resolution images. An alternating minimization iterative algorithm is proposed and developed to reconstruct the image. We give a convergence analysis of the alternating minimization iterative algorithm and show that it converges for H¹-norm regularization Functional. Numerical examples are given to illustrate the effectiveness of the joint minimization model and the efficiency of the algorithm.