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The newly emerging sampling methodology of compressed sensing opens a door to obtain compressed data directly. How to efficiently reconstruct the original signal from the compressed data becomes a new challenge. Many reconstruction works have been proposed on mono-view images by exploring the sparsity of the original image. However, it is a challenge to efficiently explore the correlations among different views in compressed multi-view imaging systems. With the aid of inter-view disparity information at receiver end, a joint reconstruction approach is presented for independently captured view-point images via compressed imaging. In the proposed approach, a robust reconstruction is obtained by formulating the occurrences of outliers, usually caused by illumination change, mismatch and discontinuity in disparity estimation, as a sparse model, which can be efficiently solved by a proximal sub-gradient algorithm bas ed on l1-norm minimization. Experimental results show that the joint reconstruction of compressed multi-view images can achieve significantly better recovery quality than the independently reconstructed ones.