Bayesian smoothing of photon-limited images with applications in astronomy
成果类型：
Article
署名作者：
White, John Thomas; Ghosal, Subhashis
署名单位：
North Carolina State University
刊物名称：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
ISSN/ISSBN：
1369-7412
DOI：
10.1111/j.1467-9868.2011.00776.x
发表日期：
2011
页码：
579-599
关键词：
摘要：
We consider a multiscale model for intensities in photon-limited images using a Bayesian framework. A typical Dirichlet prior on relative intensities is not efficient in picking up structures owing to the continuity of intensities. We propose a novel prior using the so-called 'Chinese restaurant process' to create structures in the form of equal intensities of some neighbouring pixels. Simulations are conducted using several photon-limited images, which are common in X-ray astronomy and other high energy photon-based images. Applications to astronomical images from the Chandra X-ray Observatory satellite are shown. The new methodology outperforms most existing methods in terms of image processing quality, speed and the ability to select smoothing parameters automatically.
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