Deconvolution of (x ,y, wavelength) images
Abstract
Currently, image deconvolution receives increasing attention from the academic world. However, few works have been done in deconvolution of data with heterogeneous dimensions, for example (x, y, depth, wavelength, time...). Following an inverse problem approach, we propose to use physical correlations in the wavelengths and time axes to constraint deconvolution problem. It leads to a faster and a better reconstruction than successive images deconvolution. Moreover, in some cases, it leads to a very effective blind deconvolution scheme(deconvolution of observation blurred by an unknown process). We present deconvolution of (x,y,wavelength) data cubes from the SuperNova factory. (The SuperNova factory is a survey using an integral field spectrograph to observe spectro-photometrically Type Ia supernovae (SNeIa) in the redshift range 0.03