9 October 2008 Quantitative performance evaluation of a blurring restoration algorithm based on principal component analysis
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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.
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Mario Greco, Claudia Huebner, and Gabriele Marchi "Quantitative performance evaluation of a blurring restoration algorithm based on principal component analysis", Proc. SPIE 7108, Optics in Atmospheric Propagation and Adaptive Systems XI, 71080L (9 October 2008); doi: 10.1117/12.800157; https://doi.org/10.1117/12.800157
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