Abstract
Several algorithms are used nowadays for detecting stellar objects in astronomical images, for example in the DAOPHOTprogram package and in SExtractor (Software for source extraction). Our team has become acquainted with the wavelet transform and its good localization properties. After studying the manual for DAOPHOT and SExtractor, and becoming familiar with the trous algorithm used for calculating the wavelet transform, we set ourselves the task to implement an algorithm for star detection on the basis of the wavelet transform. We focused on detecting stellar objects in complex fields, such as globular clusters and galaxies. This paper describes a stellar object detection algorithm with the help ofthe wavelet transform, and presents our results.
Highlights
The DAOPHOT program [1, 2] and SExtractor [3, 4] calculate the estimated background value and perform thresholding of each pixel: if it is more than a specified threshold and meets certain conditions, we consider it to be a light source
Stellar objects are detected in wavelet coefficients W1, W2, etc., representing details contained in the original image
The estimated noise standard deviation for each decomposition level will be used for wavelet coefficient thresholding in a way that it will set to zero all the coefficients belonging to the interval |WJ | ≤ 3σ
Summary
The DAOPHOT program [1, 2] and SExtractor [3, 4] calculate the estimated background value and perform thresholding of each pixel: if it is more than a specified threshold and meets certain conditions, we consider it to be a light source. When the wavelet transform is implemented by the Mallat algorithm [5] there is for each additional degree of decomposition an image with dimensions twice smaller than on the previous level. Wavelet transform implementation by the atrous algorithm involves convoluting the input image with a 2D convolution kernel representing a two-dimensional scaling function, which imitates the stellar PSF [6].
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