Solar hard X-ray imaging by means of Compressed Sensing and Finite Isotropic Wavelet Transform

This paper shows that compressed sensing realized by means of regularized deconvolution and the Finite Isotropic Wavelet Transform is effective and reliable in hard X-ray solar imaging. The method utilizes the Finite Isotropic Wavelet Transform with Meyer function as the mother wavelet. Further, compressed sensing is realized by optimizing a sparsity-promoting regularized objective function by means of the Fast Iterative Shrinkage-Thresholding Algorithm. Eventually, the regularization parameter is selected by means of the Miller criterion. The method is applied against both synthetic data mimicking the Spectrometer/Telescope Imaging X-rays (STIX) measurements and experimental observations provided by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI). The performances of the method are compared with the results provided by standard visibility-based reconstruction methods. The results show that the application of the sparsity constraint and the use of a continuous, isotropic framework for the wavelet transform provide a notable spatial accuracy and significantly reduce the ringing effects due to the instrument point spread functions

Compressed sensing and finite isotropic wavelets for the STIX reconstruction problem

STIX is a X-ray imaging spectroscopy device to be mounted as part of the Solar Orbiter cluster. Its goal is to provide images and spectra of solar flaring regions. The device provides 30 measurements of the incoming photon flux which can be interpreted as spatial Fourier samples. In this paper we present a method for reconstructing the intensity image from the few provided measurements. The proposed algorithm is based on compressed sensing theory. In order to provide the needed sparsity, we build an isotropic wavelet transform which is very appropriate for the STIX measurements. The evaluation on two simulated solar flares shows the potential of the algorithm in reconstructing hard X-ray maps from STIX measurements

A New Classifier Combination Scheme Using Clustering Ensemble

Combination of multiple classifiers has been shown to increase classification accuracy in many application domains. Besides, the use of cluster analysis techniques in supervised classification tasks has shown that they can enhance the quality of the classification results. This is based on the fact that clusters can provide supplementary constraints that may improve the generalization capability of the classifiers. In this paper we introduce a new classifier combination scheme which is based on the Decision Templates Combiner. The proposed scheme uses the same concept of representing the classifiers decision as a vector in an intermediate feature space and builds more representatives decision templates by using clustering ensembles. An experimental evaluation was carried out on several synthetic and real datasets. The results show that the proposed scheme increases the classification accuracy over the Decision Templates Combiner, and other classical classifier combinations methods