Iterative Identification and Restoration of Images (eBook, PDF)
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One of the most intriguing questions in image processing is the problem of recovering the desired or perfect image from a degraded version. In many instances one has the feeling that the degradations in the image are such that relevant information is close to being recognizable, if only the image could be sharpened just a little. This monograph discusses the two essential steps by which this can be achieved, namely the topics of image identification and restoration. More specifically the goal of image identifi cation is to estimate the properties of the imperfect imaging system (blur) from the...
One of the most intriguing questions in image processing is the problem of recovering the desired or perfect image from a degraded version. In many instances one has the feeling that the degradations in the image are such that relevant information is close to being recognizable, if only the image could be sharpened just a little. This monograph discusses the two essential steps by which this can be achieved, namely the topics of image identification and restoration. More specifically the goal of image identifi cation is to estimate the properties of the imperfect imaging system (blur) from the observed degraded image, together with some (statistical) char acteristics of the noise and the original (uncorrupted) image. On the basis of these properties the image restoration process computes an estimate of the original image. Although there are many textbooks addressing the image identification and restoration problem in a general image processing setting, there are hardly any texts which give an indepth treatment of the state-of-the-art in this field. This monograph discusses iterative procedures for identifying and restoring images which have been degraded by a linear spatially invari ant blur and additive white observation noise. As opposed to non-iterative methods, iterative schemes are able to solve the image restoration problem when formulated as a constrained and spatially variant optimization prob In this way restoration results can be obtained which outperform the lem. results of conventional restoration filters.
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