Suggestions from the field of image processing to compensate for turbulence effects and restore degraded images include
motion-compensated image integration after which the image can be considered as a non-distorted image that has been
blurred with a point spread function (PSF) the same size as the pixel motions due to the turbulence. Since this PSF is
unknown, a blind deconvolution is still necessary to restore the image. By utilising different blind deconvolution
algorithms along with the motion-compensated image integration, several variants of this turbulence compensation
method are created. In this paper we discuss the differences of the various blind deconvolution algorithms employed and
give a qualitative analysis of the turbulence compensation variants by comparing their respective restoration results. This
is done by visual inspection as well as by means of different image quality metrics that analyse the high frequency
components.
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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