Abstract
Among image restoration approaches, image deconvolution has been considered a powerful solution. In image deconvolution, a point spread function (PSF), which describes the blur of the image, needs to be determined. Therefore, in this paper, we propose an iterative PSF estimation algorithm which is able to estimate an accurate PSF. In real-world motion-blurred images, a simple parametric model of the PSF fails when a camera moves in an arbitrary direction with an inconsistent speed during an exposure time. Moreover, the PSF normally changes with spatial location. In order to accurately estimate the complex PSF of a real motion blurred image, we iteratively update the PSF by using a directional spreading operator. The directional spreading is applied to the PSF when it reduces the amount of the blur and the restoration artifacts. Then, to generalize the proposed technique to the linear shift variant (LSV) model, a piecewise invariant approach is adopted by the proposed image segmentation method. Experimental results show that the proposed method effectively estimates the PSF and restores the degraded images.
Highlights
Due to the degradation of the image caused by the limited performance of optical and electronic devices of in today’s cameras, the image restoration has been researched in literature
Since the original image is not given in the real case, quality metrics such as an improvement in the signalto-noise ratio (ISNR) [11] are not applicable to assess the accuracy of the point spread function (PSF)
We presented an iterative PSF estimation method and its application to linear shift variant (LSV) image restoration
Summary
Due to the degradation of the image caused by the limited performance of optical and electronic devices of in today’s cameras, the image restoration has been researched in literature. The parameters of the uniform motion blur, the length and angle, are estimated with an optimization technique Though these approaches estimate an accurate PSF, they are not applicable to the PSF which cannot be regarded as a parametric form. When moving objects exist or the scene has depth, the PSF changes with spatial location over the image [5] In this case, an estimation of the LSV PSF is essential to restore the image. Most of the current approaches with the LSV model assume that the PSF of the image has a slow varying characteristic [7, 8] With this assumption, the locally invariant PSF is estimated, and a piecewise invariant deconvolution is applied.
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