In this article, a novel low-light image enhancement (LIME) using reflection model and principal component analysis (PCA) has been proposed. The proposed algorithm works adaptively for dark images based on reflection model and multiscale principle. An input RGB color image is first stretched to correct any type of color distortion and then converted to HSV color space. By using the concept of multiscale theory, the illumination coefficient of the V component is calculated. Then, an image brightness enhancement scheme is employed based on the Fechner principle, which adaptively regulates the parameters of the enhancement function. Further to this, PCA based on image fusion approach is framed to pull out the relevant features from these two images. Finally, the contrast-limited adaptive histogram equalization (CLAHE) model is applied to improve the global contrast. In comparison with other methods, the proposed method gives better outcomes in context of subjective and objective assessments.

中文翻译：

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使用反射模型的基于主成分分析的低光图像增强


在本文中，提出了一种使用反射模型和主成分分析（PCA）的新型低光图像增强（LIME）。该算法基于反射模型和多尺度原理，自适应地适应暗图像。输入的 RGB 彩色图像首先被拉伸以纠正任何类型的颜色失真，然后转换为 HSV 颜色空间。利用多尺度理论的概念，计算V分量的照度系数。然后，采用基于费希纳原理的图像亮度增强方案，自适应调节增强函数的参数。除此之外，基于图像融合方法的 PCA 被设计来从这两个图像中提取相关特征。最后，应用对比度限制自适应直方图均衡（CLAHE）模型来提高全局对比度。与其他方法相比，所提出的方法在主观和客观评估方面给出了更好的结果。