Joint image registration and point spread function estimation for the super-resolution of satellite images

- A generic observation model that includes both geometric transformation and photometric transformation is proposed.
- Image registration and PSF estimation are combined into a single process to enable registration parameters and PSF to be estimated and improved progressively.
- LRTV regularization is incorporated into the multi-frame SR.

Image registration and point spread function (PSF) estimation are both key steps in image super-resolution (SR). Traditionally, these two steps are treated independently, which is adequate for natural images. However, for satellite images, which commonly suffer from focal plane distortion and unrecorded spacecraft jitter, it is always difficult to achieve satisfactory image registration or PSF estimation. Consequently, the errors brought by these two processes significantly affect each other and degrade the quality of the subsequent high-resolution (HR) reconstruction. In this paper, a novel joint image registration and PSF estimation method is proposed to produce HR images from a set of degraded low-resolution (LR) satellite images. The joint SR approach is formulated as a convex optimization problem which minimizes the combination of these two parts. It is aimed at achieving PSF estimation and registration simultaneously and progressively, to handle the error in different levels. In addition, the proposed method adopts a more generic observation model, including both geometric motion and radiation difference, which makes the model more universal. Moreover, an iterative scheme based on alternating minimization (AM) is developed to solve the presented cost function via simultaneous low-rank and total variation (LRTV) regularizations. The experimental results confirm the effectiveness of the proposed method on both simulated data and real satellite images.