SIAM Journal on Imaging Sciences, Volume 13, Issue 1, Page 1-28, January 2020.
Contrast enhancement plays an important role in image/video processing and computer vision applications. Its main purpose is to adjust the image intensity to enhance the quality and features of the image. In this paper, we propose a simple and efficient adaptive variational model for contrast enhancement for partially shaded low-light images. The key idea of this adaptive approach is to employ the maximum image of the RGB color channels as a classifier to divide the image domain into the relatively bright and dim parts, and then use different fitting terms for each part such that the bright pixels are preserved as close as possible to the original ones while the dim pixels are boosted with brightness and contrast-level parameters to adjust the degree of the strength. With this adaptivity, one can find that the proposed model considerably improves upon the existing variational models in the literature. In this paper, the existence and uniqueness of the minimizer for the variational minimization problem is established. The split Bregman method is used to accomplish an efficient numerical implementation of the adaptive variational model. Moreover, a number of numerical experiments and comparisons with other popular enhancement methods are conducted to demonstrate the high performance of the newly proposed method.

中文翻译：

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弱光图像对比度增强的自适应变分模型

SIAM影像科学杂志，第13卷，第1期，第1-28页，2020年1月。
对比度增强在图像/视频处理和计算机视觉应用中起着重要作用。其主要目的是调整图像强度以增强图像的质量和功能。在本文中，我们提出了一种简单有效的自适应变分模型来增强部分阴影的弱光图像的对比度。这种自适应方法的关键思想是采用RGB颜色通道的最大图像作为分类器，将图像域划分为相对明亮和暗淡的部分，然后对每个部分使用不同的拟合项，以便保留明亮像素尽可能接近原始像素，同时使用亮度和对比度级别参数增强暗像素以调整强度。有了这种适应性，可以发现，所提出的模型大大改进了文献中现有的变异模型。本文建立了变分最小化问题最小化器的存在性和唯一性。分裂布雷格曼方法用于完成自适应变分模型的有效数值实现。此外，进行了许多数值实验并与其他流行的增强方法进行了比较，以证明新提出的方法的高性能。