Applying rotation-invariant star descriptor to deep-sky image registration

Abstract

Image registration is a critical process of many deep-sky image processing applications. Image registration methods include image stacking to reduce noise or achieve long exposure effects within a short exposure time, image stitching to extend the field of view, and atmospheric turbulence removal. The most widely used method for deep-sky image registration is the triangle- or polygon-based method, which is both memory and computation intensive. Deepsky image registration mainly focuses on translation and rotation caused by the vibration of imaging devices and the Earth’s rotation, where rotation is the more difficult problem. For this problem, the best method is to find corresponding rotation-invariant features between different images. In this paper, we analyze the defects introduced by applying rotation-invariant feature descriptors to deep-sky image registration and propose a novel descriptor. First, a dominant orientation is estimated from the geometrical relationships between a described star and two neighboring stable stars. An adaptive speeded-up robust features (SURF) descriptor is then constructed. During the construction of SURF, the local patch size adaptively changes based on the described star size. Finally, the proposed descriptor is formed by fusing star properties, geometrical relationships, and the adaptive SURF. Extensive experiments demonstrate that the proposed descriptor successfully addresses the gap resulting from applying the traditional feature-based method to deep-sky image registration and performs well compared to state-of-the-art descriptors.

Keywords

image registration / feature descriptor / deep-sky image / rotation-invariant descriptor

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