Quantitative performance evaluation of a blurring restoration algorithm based on principal component analysis
Mario Greco, Claudia S. Huebner, Gabriele Marchi · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
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.