SIRF: Simultaneous Image Registration and Fusion in A Unified Framework

TL;DR

This paper introduces a unified convex optimization framework for simultaneous image registration and fusion, enhancing spectral and spatial quality in satellite image processing.

Contribution

The paper presents a novel convex optimization approach that jointly registers and fuses images, incorporating dynamic gradient sparsity for improved edge preservation.

Findings

Outperforms seven state-of-the-art methods in spatial and spectral quality

Efficient algorithm with linear complexity per iteration

Effective on coarsely registered real-world datasets

Abstract

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.