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.Introduction: The multi-camera network (MCN) has received significant attention in applications such as surveillance, and robotics. The recently developed distributed compressed sensing (DCS) [1], the extension of compressed sensing (CS), can help address the challenge of acquiring higher-resolution images with incomplete sample data in the traditional MCN. The DCS decoding reconstructs all signals jointly by exploiting inter-signal correlations through the concept of joint sparsity. But these joint sparsity models are not trivial for image ensembles. Some researchers [2,3] have investigated the multi-view correlation and presented the relevant reconstruction methods. However, these methods either require estimating the relative camera positions or do not perform the reconstructions simultaneously. To date, how to reconstruct multi-view images jointly, intuitively and simultaneously via CS framework, remains a critical problem.In this Letter, we assume that the relative camera positions are unknown and the neighbouring multi-view images are visually similar. The correlation is captured by introducing a regularisation term, which encourages the sparse prior of the difference images between two contiguous multi-view images. Then the joint reconstruction is formulated as an unconstrained optimisation problem. Moreover, an effective iterative algorithm is presented to solve the optimisation problem.
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