How to co-add images? I. A new iterative method for image reconstruction of dithered observations  

作　　者：Lei Wang Guo-Liang Li Lei Wang Guo-Liang Li(Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 210008, China)

机构地区：[1]Purple Mountain Observatory, Chinese Academy of Sciences

出　　处：《Research in Astronomy and Astrophysics》2017年第10期1-14,共14页天文和天体物理学研究（英文版）

基　　金：supported by the National Basic Research Program of China (973 program, Nos. 2015CB857000 and 2013CB834900);the Foundation for Distinguished Young Scholars of Jiangsu Province (No. BK20140050);the ‘Strategic Priority Research Program the Emergence of Cosmological Structure’ of the CAS (No. XDB09010000);the National Natural Science Foundation of China (Nos. 11333008, 11233005, 11273061 and 11673065)

摘　　要：By employing the previous Voronoi approach and replacing its nearest neighbor approx- imation with Drizzle in iterative signal extraction, we develop a fast iterative Drizzle algorithm, namedfiDrizzle, to reconstruct the underlying band-limited image from undersampled dithered frames. Compared with the existing iDrizzle, the new algorithm improves rate of convergence and accelerates the computational speed. Moreover, under the same conditions （e.g. the same number of dithers and iterations）, fiDrizzle can make a better quality reconstruction than iDrizzle, due to the newly discov- ered High Sampling caused Decelerating Convergence （HSDC） effect in the iterative signal extraction process.fiDrizzle demonstrates its powerful ability to perform image deconvolution from undersampled dithers.By employing the previous Voronoi approach and replacing its nearest neighbor approx- imation with Drizzle in iterative signal extraction, we develop a fast iterative Drizzle algorithm, namedfiDrizzle, to reconstruct the underlying band-limited image from undersampled dithered frames. Compared with the existing iDrizzle, the new algorithm improves rate of convergence and accelerates the computational speed. Moreover, under the same conditions （e.g. the same number of dithers and iterations）, fiDrizzle can make a better quality reconstruction than iDrizzle, due to the newly discov- ered High Sampling caused Decelerating Convergence （HSDC） effect in the iterative signal extraction process.fiDrizzle demonstrates its powerful ability to perform image deconvolution from undersampled dithers.

关 键 词：techniques： image processing -- methods： observational -- stars： imaging -- planets andsatellites： detection -- gravitational lensing 

分 类 号：P11[天文地球—天文学] TP391.41[自动化与计算机技术—计算机应用技术]