In weak-light environments, images suffer from low contrast and the loss of details. Traditional image enhancement models are usually failure to avoid the issue of overenhancement. In this paper, a simple and novel correction method is proposed based on an adaptive local gamma transformation and color compensation, which is inspired by the illumination reflection model. Our proposed method converts the source image into YUV color space, and the component is estimated with a fast guided filter. The local gamma transform function is used to improve the brightness of the image by adaptively adjusting the parameters. Finally, the dynamic range of the image is optimized by a color compensation mechanism and a linear stretching strategy. By comparing with the state-of-the-art algorithms, it is demonstrated that the proposed method adaptively reduces the influence of uneven illumination to avoid overenhancement and improve the visual effect of low-light images.

中文翻译：

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基于自适应局部伽马变换和颜色补偿的弱光图像增强方法

在弱光环境中，图像对比度低，细节丢失。传统的图像增强模型通常无法避免过度增强的问题。在本文中，受光照反射模型的启发，提出了一种基于自适应局部伽马变换和颜色补偿的简单而新颖的校正方法。我们提出的方法将源图像转换为 YUV 颜色空间，并且用快速引导滤波器估计分量。局部伽马变换函数用于通过自适应调整参数来提高图像的亮度。最后，通过颜色补偿机制和线性拉伸策略优化图像的动态范围。通过与最先进的算法进行比较，证明了所提出的方法自适应地降低了光照不均匀的影响，避免了过度增强并改善了低光图像的视觉效果。