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Joint Deblurring and Demosaicing Using Edge Information from Bayer Images

机译:使用来自拜耳图像的边缘信息进行联合去模糊和去马赛克

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Most images obtained with imaging sensors contain Bayer patterns and suffer from blurring caused by the lens. In order to convert a blurred Bayer-patterned image into a viewable image, demosaicing and deblurring are needed. These concepts have been major research areas in digital image processing for several decades. Despite their importance, their performance and efficiency are not satisfactory when considered independently. In this paper, we propose a joint deblurring and demosaicing method in which edge direction and edge strength are estimated in the Bayer domain and then edge adaptive deblurring and edge-oriented interpolation are performed simultaneously from the estimated edge information. Experimental results show that the proposed method produces better image quality than conventional algorithms in both objective and subjective terms.
机译:使用成像传感器获得的大多数图像都包含拜耳图案,并且会受到镜头造成的模糊影响。为了将模糊的拜耳图案图像转换成可见图像,需要去马赛克和去模糊。这些概念几十年来一直是数字图像处理的主要研究领域。尽管它们很重要,但当单独考虑它们的性能和效率时却不能令人满意。在本文中,我们提出了一种联合去模糊和去马赛克方法,其中在拜耳域中估计边缘方向和边缘强度,然后从估计的边缘信息中同时执行边缘自适应去模糊和面向边缘的内插。实验结果表明,该方法在主观和主观方面均比常规算法产生更好的图像质量。

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