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Blind deblurring from single motion image based on adaptive weighted total variation algorithm

机译：基于自适应加权总变异算法的单幅运动图像盲去模糊

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Blind image deblurring is an important topic which is widely used in many research fields such as photography, optics, astronomy, medical images, monitoring, military and so on. Although many algorithms have been proposed to improve the deblurring result in the past years, most of them cannot perform perfectly in some challenging cases. This study presents a novel blind deblurring method based on an adaptive weighted total variation (TV) algorithm. The blur kernel estimation is based on the image structure, the sparsity and continuity prior of point spread function is also taken into account. To get better effect of removing the ringing artefacts, adaptive weight calculated according to the property of the higher-order partial derivatives in the local image is proposed in TV algorithm to alleviate the ill-posed inverse problem and stabilise the solution for latent image restoration. The experimental results prove that the proposed algorithm can suppress the ringing artefacts to a great extent in the latent image, and can get much better effect in both vision and theoretical results than traditional algorithms.
机译：盲图像去模糊是一个重要的课题，已广泛应用于摄影，光学，天文学，医学图像，监控，军事等许多研究领域。尽管在过去几年中提出了许多算法来改善去模糊效果，但是大多数算法在某些具有挑战性的情况下仍无法完美执行。这项研究提出了一种新的基于自适应加权总变化量（TV）算法的盲去模糊方法。模糊核估计基于图像结构，还考虑了点扩散函数的稀疏性和连续性。为了获得更好的消除振铃效果，TV算法提出了根据局部图像中高阶偏导数的性质计算出的自适应权重，以减轻不适定的逆问题，稳定潜在图像的复原解。实验结果表明，与传统算法相比，所提算法可以在很大程度上抑制潜像中的振铃伪影，并且在视觉效果和理论效果上均具有更好的效果。

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