In this paper, we introduce a novel multi-view image denoising algorithm using 3D focus image stacks (3DFIS) to exploit image redundancy within and across views. Robust disparity map is first estimated using the 3DFIS with texture-based view selection and patch-size variation scheme. Leveraging both 3DFIS and the estimated disparity map, the proposed algorithm effectively denoises the target view from multiple views through a low rank minimization approach that incorporates robust similarity metrics and occlusion handling techniques. The paper combs through a number of existing image denoising methods, including the preliminary results in our earlier research efforts, and then details the ways, means and merits of our proposed algorithm. With extensive experiments, we conclude that this novel algorithm is superior over various existing state-of-the-art approaches in terms of both visual and quantitative performance.

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