SIRF: Simultaneous Image Registration and Fusion in A Unified Framework
This addresses image fusion for remote sensing applications, providing high-quality products from coarsely registered datasets, but it is incremental as it builds on existing optimization and registration techniques.
The paper tackles the problem of fusing high-resolution panchromatic and low-resolution multispectral images by formulating it as a convex optimization problem with a dynamic gradient sparsity regularizer, and it simultaneously registers the images during fusion, achieving linear computational complexity per iteration and outperforming seven state-of-the-art methods in spatial and spectral qualities on datasets from four satellites.
In this paper, we propose a novel method for image fusion with a high-resolution panchromatic image and a low-resolution multispectral image at the same geographical location. The fusion is formulated as a convex optimization problem which minimizes a linear combination of a least-squares fitting term and a dynamic gradient sparsity regularizer. The former is to preserve accurate spectral information of the multispectral image, while the latter is to keep sharp edges of the high-resolution panchromatic image. We further propose to simultaneously register the two images during the fusing process, which is naturally achieved by virtue of the dynamic gradient sparsity property. An efficient algorithm is then devised to solve the optimization problem, accomplishing a linear computational complexity in the size of the output image in each iteration. We compare our method against seven state-of-the-art image fusion methods on multispectral image datasets from four satellites. Extensive experimental results demonstrate that the proposed method substantially outperforms the others in terms of both spatial and spectral qualities. We also show that our method can provide high-quality products from coarsely registered real-world datasets. Finally, a MATLAB implementation is provided to facilitate future research.