通过l0正则化强度和梯度先验去模糊文本图像

Jin-shan Pan, Zhe Hu, Zhixun Su, Ming-Hsuan Yang
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引用次数: 404

摘要

我们提出了一个简单而有效的基于强度和梯度的l0正则化先验文本图像去模糊。所提出的图像先验是通过观察文本图像的不同属性来激发的。在此基础上,我们开发了一种高效的优化方法来生成可靠的核估计中间结果。该方法不需要任何复杂的滤波策略来选择对最先进的去模糊算法至关重要的显著边缘。我们讨论了与其他基于边缘选择的去模糊算法的关系,并提供了如何以更有原则的方式选择显著边缘的见解。在最后的潜在图像恢复步骤中,我们开发了一种简单的方法来去除伪影并呈现更好的去模糊图像。实验结果表明,该算法与现有的文本图像去模糊方法相比具有较好的效果。此外,我们还证明了该方法可以有效地应用于低照度图像的去模糊。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deblurring Text Images via L0-Regularized Intensity and Gradient Prior
We propose a simple yet effective L0-regularized prior based on intensity and gradient for text image deblurring. The proposed image prior is motivated by observing distinct properties of text images. Based on this prior, we develop an efficient optimization method to generate reliable intermediate results for kernel estimation. The proposed method does not require any complex filtering strategies to select salient edges which are critical to the state-of-the-art deblurring algorithms. We discuss the relationship with other deblurring algorithms based on edge selection and provide insight on how to select salient edges in a more principled way. In the final latent image restoration step, we develop a simple method to remove artifacts and render better deblurred images. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art text image deblurring methods. In addition, we show that the proposed method can be effectively applied to deblur low-illumination images.
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