Multispectral Image Super-Resolution Using Structure-Guided RGB Image Fusion

Due to hardware limitation, multispectral imaging device usually cannot achieve high spatial resolution. To address the issue, this paper proposes a multispectral image super-resolution algorithm by fusing the low-resolution multispectral image and the high-resolution RGB image. The fusion is formulated as an optimization problem according to the linear image degradation models. Meanwhile, the fusion is guided by the edge structure of RGB image via the directional total variation regularizer. Then the fusion problem is solved by the alternating direction method of multipliers algorithm through iteration. The subproblems in each iterative step is simple and can be solved in closed-form. The effectiveness of the proposed algorithm is evaluated on both public datasets and our image set. Experimental results validate that the algorithm outperforms the state-of-the-arts in terms of both reconstruction accuracy and computational efficiency.