Reduction of Blur in Image by Hybrid De-convolution using Point Spread Function
Автор: Neethu M. Sasi, Jayasree V.K.
Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp
Статья в выпуске: 6 vol.8, 2016 года.
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Blurring of images is an unwelcome phenomenon that is difficult to avoid in many situations. It degrades the quality of a variety of images, including real life photographic images, astronomical images and medical images. In this paper a new image de-blurring algorithm is proposed using Lucy Richardson method. De-blurring is performed in two stages. To arrive at the best guestimate, an iterative method is employed as an initial step which computes the maximum likelihood estimate of the point spread function (PSF) without any prior information. In the second step, Lucy Richardson algorithm takes the PSF estimated in the initial step as its input parameter. In particular, for better processing of the image, suitable color space identification is done as a pre-processing step. This makes use of the idea of edge detectors. This paper, as a significant contribution, proposes a de-blurring technique, which uses a hybrid de-convolution method with a color space identification stage. This enables its application for a broad spectrum of images from real life photographic images to single photon emission computed tomography images as well. The performance of the algorithm is compared against other existing de-blurring algorithms and the results prove a better output in terms of blur reduction. Standard test images and real medical images are used for appraising the algorithm.
Blind image de-convolution, Canny edge detector, Lucy-Richardson algorithm, Maximum likelihood estimate
Короткий адрес: https://sciup.org/15013984
IDR: 15013984
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