期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2013
卷号：4
期号：2
页码：274-279
语种：English
出版社：Ayushmaan Technologies
摘要：One of the big challenges in digital photography is motion blur. To remove blur, we need: (i) To estimate how the image is blurred (i.e. the blur kernel or the point-spread function) and (ii) To restore a natural looking image through deconvolution. The blur kernel estimation is challenging because the algorithm needs to distinguish the correct image pair from incorrect ones that can also adequately explain the blurred image. The process of deconvolution is also difficult because the algorithm needs to restore high frequency image contents attenuated by blur. In this paper, we address a few aspects of these challenges. We introduce an insight that a blur kernel can be estimated by analyzing edges in a blurred photograph. We can recover the blur using the inverse radon transform. This method is computationally attractive and is well suited to images with many edges. Blurred edge profiles can also serve as additional cues for existing kernel estimation algorithms. We have introduced a method to integrate this information into a maximum-a-posteriori kernel estimation framework, and show its benefits. In this paper, we compared restored gaussian blurred images, by using four types of techniques of deblurring images such as Wiener filter, Inverse filter, Lucy Richardson deconvolution algorithm and our purposed algorithm on the basis of well-known image quality assessment parameters like mean squared error (MSE) and peak signal-to-noise ratio (PSNR).
