Abstract
Sofar we have assumed that the PSF of the image formation system, the variance of the observation noise, and a model for the original image were known prior to the restoration process. In many practical situations of interest these parameters are, however, not available. Image identification (sometimes referred to as blur identification, image-blur identification, or a posteriori restoration) focuses on developing estimation procedures, which identify all the information that is required to restore an image from the noisy blurred image itself.
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© 1991 Springer Science+Business Media New York
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Lagendijk, R.L., Biemond, J. (1991). Maximum Likelihood Image Identification. In: Iterative Identification and Restoration of Images. The Springer International Series in Engineering and Computer Science, vol 118. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-3980-3_6
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DOI: https://doi.org/10.1007/978-1-4615-3980-3_6
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-6778-9
Online ISBN: 978-1-4615-3980-3
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