Thanks to its ability to yield functionally rather than anatomically-based information, the single photon emission computed tomography (SPECT) imagery technique has become a great help in the diagnostic of cerebrovascular diseases which are the third most common cause of death in the USA and Europe. Nevertheless, SPECT images are very blurred and consequently their interpretation is difficult. In order to improve the spatial resolution of these images and then to facilitate their interpretation by the clinician, we propose to implement and to compare the effectiveness of different existing ‘‘blind’’ or ‘‘supervised’’ deconvolution methods. To this end, we present an accurate distribution mixture parameter estimation procedure which takes into account the diversity of the laws in the distribution mixture of a SPECT image. In our application, parameters of this distribution mixture are efficiently exploited in order to prevent overfitting of the noisy data for the iterative deconvolution techniques without regularization term, or to determine the exact support of the object to be restored when this one is needed. Recent blind deconvolution techniques such as the NAS–RIF algorithm, [D. Kundur and D. Hatzinakos, ‘‘Blind image restoration via recursive filtering using deterministic constraints,’’ in Proc. International Conf. On Acoustics, Speech, and Signal Processing, Vol. 4, pp. 547–549 (1996).] combined with this estimation procedure, can be efficiently applied in SPECT imagery and yield promising results.