Liu Shouxin, Long Wei, He Lei, Li Yanyan, Ding Wei
School of Mechanical Engineering, Sichuan University, Chengdu 610065, China.
Entropy (Basel). 2021 Jun 13;23(6):746. doi: 10.3390/e23060746.
We proposed the Retinex-based fast algorithm (RBFA) to achieve low-light image enhancement in this paper, which can restore information that is covered by low illuminance. The proposed algorithm consists of the following parts. Firstly, we convert the low-light image from the RGB (red, green, blue) color space to the HSV (hue, saturation, value) color space and use the linear function to stretch the original gray level dynamic range of the V component. Then, we estimate the illumination image via adaptive gamma correction and use the Retinex model to achieve the brightness enhancement. After that, we further stretch the gray level dynamic range to avoid low image contrast. Finally, we design another mapping function to achieve color saturation correction and convert the enhanced image from the HSV color space to the RGB color space after which we can obtain the clear image. The experimental results show that the enhanced images with the proposed method have better qualitative and quantitative evaluations and lower computational complexity than other state-of-the-art methods.
在本文中，我们提出了基于视网膜皮层模型的快速算法（RBFA）来实现低光照图像增强，该算法能够恢复被低照度覆盖的信息。所提出的算法由以下部分组成。首先，我们将低光照图像从RGB（红、绿、蓝）颜色空间转换到HSV（色调、饱和度、明度）颜色空间，并使用线性函数拉伸V分量的原始灰度动态范围。然后，我们通过自适应伽马校正估计光照图像，并使用视网膜皮层模型实现亮度增强。之后，我们进一步拉伸灰度动态范围以避免图像对比度低。最后，我们设计另一个映射函数来实现颜色饱和度校正，并将增强后的图像从HSV颜色空间转换到RGB颜色空间，由此我们可以获得清晰的图像。实验结果表明，与其他现有先进方法相比，用所提出的方法增强后的图像具有更好的定性和定量评估，且计算复杂度更低。
