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Image enhancement using divide-and-conquer strategy

机译：使用分而治之策略进行图像增强

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Existing enhancement methods tend to overlook the difference between image components of low frequency and high-frequency. However, image low-frequency portions contain smooth areas occupied the majority of the image, while high-frequency components are sparser in the image. Meanwhile, the different importance of image low-frequency and high-frequency components cannot be precisely and effectively for image enhancement. Therefore, it is reasonable to deal with these components separately when designing enhancement algorithms with image subspaces. In this paper, we propose a novel divide and-conquer strategy to decompose the observed image into four subspaces and enhance the images corresponding to each subspace individually. We employ the existing technique of gradient distribution specification for these enhancements, which has displayed promising results for image naturalization. We then reconstruct the full image using the weighted fusion of these four subspace images. Experimental results demonstrate the effectiveness of the proposed strategy in both image naturalization and details promotion. (C) 2017 Elsevier Inc. All rights reserved.
机译：现有的增强方法倾向于忽略低频和高频的图像分量之间的差异。但是，图像低频部分包含占据图像大部分区域的平滑区域，而高频分量在图像中较为稀疏。同时，图像低频和高频分量的不同重要性对于图像增强不能精确有效地实现。因此，在设计具有图像子空间的增强算法时，合理地分开处理这些组件是合理的。在本文中，我们提出了一种新颖的分而治之的策略，将观察到的图像分解为四个子空间，并分别增强与每个子空间相对应的图像。我们对这些增强功能采用了现有的梯度分布规范技术，该技术已为图像自然化显示了有希望的结果。然后，我们使用这四个子空间图像的加权融合来重建完整图像。实验结果证明了该策略在图像自然化和细节提升方面的有效性。 （C）2017 Elsevier Inc.保留所有权利。

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