Abstract
In the field on blind image deconvolution a new promising algorithm, based on the Principal Component Analysis
(PCA), has been recently proposed in the literature. The main advantages of the algorithm are the following:
computational complexity is generally lower than other deconvolution techniques (e.g., the widely used Iterative Blind
Deconvolution - IBD - method); it is robust to white noise; only the blurring point spread function support is required to
perform the single-observation deconvolution (i.e., a single degraded observation of a scene is available), while the
multiple-observation one is completely unsupervised (i.e., multiple degraded observations of a scene are available). The
effectiveness of the PCA-based restoration algorithm has been only confirmed by visual inspection and, to the best of our
knowledge, no objective image quality assessment has been performed. In this paper a generalization of the original
algorithm version is proposed; then the previous unexplored issue is considered and the achieved results are compared
with that of the IBD method, which is used as benchmark.
