Multi-Frame Super-Resolution of Gaofen-4 Remote Sensing Images

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.