| Summary: | 碩士 === 國立成功大學 === 電腦與通信工程研究所 === 96 === Digital images are more and more popular now, and the restoration of the degraded images becomes more important. In the existed methods for image restoration, the informations about the degraded process are supposed to be known. However the informations of the degraded image is not always known in practice. Thus we must use the properties or statistics of the degraded images to restore the these images.
The process of image restoration, which uses only partial or no information about the degraded process, are often the so-called blind image deconvolution. Most algorithms of blind image deconvolution use iterative methods to estimate the point spread function (PSF) and the original image of the degraded model. However, most of these methods have heavy computation complexity. Furthermore some algorithms even converge to a unpredicted result in a certain situation. Therefore it is a important issue to increase the stability of restoration algorithms.
In this thesis, we use the concept of independent component analysis (ICA) to analyze the independence of images. We also use this concept to estimate the values of the parameters of PSFs and the original image. Experimental results present that the estimated values of parameters is closed to the real values, and our algorithm is also stable for various images.
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