Abstract
Gaofen-4 is China’s first geosynchronous orbit high-definition optical imaging satellite with extremely high temporal resolution. The features of staring imaging and high temporal resolution enable the super-resolution of multiple images of the same scene. In this paper, we propose a super-resolution (SR) technique to reconstruct a higher-resolution image from multiple low-resolution (LR) satellite images. The method first performs image registration in both the spatial and range domains. Then the point spread function (PSF) of LR images is parameterized by a Gaussian function and estimated by a blind deconvolution algorithm based on the maximum a posteriori (MAP). Finally, the high-resolution (HR) image is reconstructed by a MAP-based SR algorithm. The MAP cost function includes a data fidelity term and a regularized term. The data fidelity term is in the L2 norm, and the regularized term employs the Huber-Markov prior which can reduce the noise and artifacts while preserving the image edges. Experiments with real Gaofen-4 images show that the reconstructed images are sharper and contain more details than Google Earth ones.
Highlights
Images with higher resolution are required in most electronic imaging applications, such as remote sensing, medical diagnostics, and video surveillance
We propose a modified maximum a posteriori (MAP)-based multi-frame blind deconvolution algorithm to estimate the point spread function (PSF), which was originally proposed by Matson et al [27]
We propose an SR algorithm to reconstruct an HR image from multiple LR
Summary
Images with higher resolution are required in most electronic imaging applications, such as remote sensing, medical diagnostics, and video surveillance. The basic idea of SR is that the LR images of the same scene contain different information because of relative sub-pixel shifts, an HR image with higher spatial information can be reconstructed by image fusion. Numerous SR algorithms have been proposed since the concept of SR was introduced by Tsai and Huang [4] in the frequency domain. Most of researchers nowadays address the problem mainly in the spatial domain, because it is more flexible to model all kinds of image degradations [5]. Ur and Gross [6] proposed a non-uniform interpolation of multiple spatially-shifted LR images based on the generalized multichannel sampling theorem, followed by a deblurring process. The advantage of the POCS method is that it allows a convenient inclusion of prior knowledge into the reconstruction process. Irani and Peleg [9]
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