Compressed sensing based remote sensing image reconstruction using an auxiliary image as priors

In remote sensing applications, there are often multi-source or multi-temporal images whose different components are acquired separately. Therefore, a part of the acquired images in multi-component data can be used as priors. In this paper, the reconstruction of a remote sensing image using an auxiliary image from another sensor or another time as the reference is considered. For this application, a new compressed sensing object function with an reference image as a prior is developed. In the new model, the sparsity constraints in transform domain comes from the target image, and the gradient priors in spatial domain comes from auxiliary reference image. To optimizing the the hybrid regularization, the algorithm is based on Bregman split method. The performance of the algorithm is evaluated both qualitatively and quantitatively. The results of experiment confirm that the proposed algorithm gets higher peak signal to noise ratio (PSNR) than other approaches without reference images as priors.