Point object recognition — Some single- and multi-channel applications

Peter Linde, Ralph Snel, Stefan Spännare
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引用次数: 1

Abstract

High-precision photometric analysis of images of crowded stellar fields needs sophisticated algorithms. The photometric precision is, however, a strong function of the completeness of source detection. We discuss several aspects of this problem, both with relation to single- and multi-channel applications. In single images, we separate detection into two phases, source image enhancement and actual detection. Comparative tests show a point spread function pixel fitting technique to give the best results. For the fraction of undetectable stars still affecting image statistics, we have developed a technique to extract information about their effect on the faint-end luminosity function. We give two examples of multi-channel image fusion applications: (1) a combination of low- and high-resolution images and (2) removal of undersampling effects by sub-pixel image displacements. Preliminary results show considerable potential for these techniques.

点对象识别。一些单通道和多通道应用
密集恒星场图像的高精度光度分析需要复杂的算法。然而,光度精度是光源检测完整性的一个重要指标。我们讨论了这个问题的几个方面,包括与单通道和多通道应用的关系。在单幅图像中,我们将检测分为源图像增强和实际检测两个阶段。对比试验表明,采用点扩展函数像素拟合技术可以获得最佳的拟合效果。对于仍然影响图像统计的不可探测恒星的部分,我们开发了一种技术来提取它们对暗端光度函数的影响信息。我们给出了两个多通道图像融合应用的例子:(1)低分辨率和高分辨率图像的组合;(2)通过亚像素图像位移去除欠采样效应。初步结果显示这些技术具有相当大的潜力。
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