Pan-Sharpening With a Hyper-Laplacian Penalty

Yiyong Jiang, Xinghao Ding, Delu Zeng, Yue Huang, John Paisley; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 540-548

Pan-sharpening is the task of fusing spectral information in low resolution multispectral images with spatial information in a corresponding high resolution panchromatic image. In such approaches, there is a trade-off between spectral and spatial quality, as well as computational efficiency. We present a method for pan-sharpening in which a sparsity-promoting objective function preserves both spatial and spectral content, and is efficient to optimize. Our objective incorporates the L1/2-norm in a way that can leverage recent computationally efficient methods, and L1 for which the alternating direction method of multipliers can be used. Additionally, our objective penalizes image gradients to enforce high resolution fidelity, and exploits the Fourier domain for further computational efficiency. Visual quality metrics demonstrate that our proposed objective function can achieve higher spatial and spectral resolution than several previous well-known methods with competitive computational efficiency.