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A piecewise-based contrast enhancement framework for low lighting video

机译:基于分段的对比度增强框架,用于低照度视频

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摘要

In this paper, we propose an efficient automatic contrast enhancement algorithm for low lighting video. The algorithm is based on a piecewise stretch on the brightness component extracted with Retinex theory in HSV space to improve the visuality of the image. By dividing the brightness component into dark and bright part, nonlinear transformations with different distribution assumption were performed respectively. All the model parameters were estimated automatically according to the illumination conditions. We use two methods to estimate the brightness. The one is global illumination estimation and the other is local illumination estimation. In comparison with global estimation, a local illumination estimation method is proposed for the further improvement. Experiments show that the algorithm can achieve satisfactory effect for nighttime image or video enhancement by comparing with some state-of-the-art approaches.
机译:在本文中,我们提出了一种有效的低对比度视频自动对比度增强算法。该算法基于在HSV空间中使用Retinex理论提取的亮度分量的分段拉伸以提高图像的可视性。通过将亮度分量分为暗和亮部分,分别进行了具有不同分布假设的非线性变换。根据光照条件自动估算所有模型参数。我们使用两种方法来估计亮度。一个是全局照明估计,另一个是局部照明估计。与全局估计相比,提出了一种局部照度估计方法以进行进一步的改进。实验表明,通过与一些最新方法进行比较,该算法可以在夜间图像或视频增强方面取得令人满意的效果。

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