A combined multiresolution approach for faint source extraction from inrared astronomical raw images sequence

Faint sources detection is one of the major issues during the reconstruction of an astronomical science image from a raw data sequence. This problem is a consequence of the detection limit of the infrared instruments as well as the number of cosmic ray impacts (glitches) that leads to the false detection. Astronomical images contain many objects with isotropic structures (e.g. point sources) but also plenty of anisotropic information (e.g. filamentary structures). The wavelet transform is usually applied to separate all these signal constituents in each pixel, then a map is built to represent the information of the associated noise before applying a source detection algorithm. Wavelets are well adapted to point singularities (discontinuities), however, they have a problem with orientation selectivity. Therefore, they do not represent anisotropic structures (e.g. smooth curves) effectively. This paper presents a combined approach conlourlet-wavelet for faint source extraction from infrared raw- images sequences. While the contourlet representation provides oriented support for efficient approximation of anisotropic structures, isotropic geometry is effectively captured by separable wavelets. This novel approach has been tested on real and simulated infrared images, stemming from the infrared space observatory database