Image deblurring has long been modeled as a deconvolution problem. In the literature, the point-spread function (PSF) is often assumed to be known exactly. However, in practical situations such as image acquisition in cameras, we may have incomplete knowledge of the PSF. This deblurring problem is referred to as blind deconvolution. We employ a statistical point of view of the data and use a modified maximum <i>a posteriori</i> approach to identify the most probable object and blur given the observed image. To facilitate computation we use an iterative method, which is an extension of the traditional expectation–maximization method, instead of direct optimization. We derive separate formulas for the updates of the estimates in each iteration to enhance the deconvolution results, which are based on the specific nature of our <i>a priori</i> knowledge available about the object and the blur.
© 2000 Optical Society of America
(000.5490) General : Probability theory, stochastic processes, and statistics
(100.1830) Image processing : Deconvolution
(100.2000) Image processing : Digital image processing
(100.3020) Image processing : Image reconstruction-restoration
(110.5200) Imaging systems : Photography
Edmund Y. Lam and Joseph W. Goodman, "Iterative statistical approach to blind image deconvolution," J. Opt. Soc. Am. A 17, 1177-1184 (2000)