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The problem of compressed sensing joint reconstruction of multi-view images in camera networks is considered. Noting that the neighbouring images are visually similar, the multi-view correlation is captured by the sparse prior of the difference images between two contiguous multi-view images. Thus the joint reconstruction is formulated as an unconstrained optimisation problem, which contains a quadratic fidelity term and two regularisation terms encouraging the sparse priors for multi-view images and their difference images, respectively. Moreover, an effective iterative algorithm is presented to solve the optimisation problem. Experimental results with the real multi-view images show that the proposed method can perform joint reconstruction with greater accuracy than CS image-by-image reconstruction.