Zhang Yu, Di Xiaoguang, Zhang Bin, Ji Ruihang, Wang Chunhui
IEEE Trans Image Process. 2022;31:759-772. doi: 10.1109/TIP.2021.3135473. Epub 2021 Dec 28.
Low-light images suffer from severe noise, low brightness, low contrast, etc. In previous researches, many image enhancement methods have been proposed, but few methods can deal with these problems simultaneously. In this paper, to solve these problems simultaneously, we propose a low-light image enhancement method that can be combined with supervised learning and previous HSV (Hue, Saturation, Value) or Retinex model-based image enhancement methods. First, we analyse the relationship between the HSV color space and the Retinex theory, and show that the V channel (V channel in HSV color space, equals the maximum channel in RGB color space) of the enhanced image can well represent the contrast and brightness enhancement process. Then, a data-driven conditional re-enhancement network (denoted as CRENet) is proposed. The network takes low-light images as input and the enhanced V channel (V channel of the enhanced image) as a condition during testing, and then it can re-enhance the contrast and brightness of the low-light image and at the same time reduce noise and color distortion. In addition, it takes 23 ms to process a color image with the resolution 400*600 on a 1080Ti GPU. Finally, some comparative experiments are implemented to prove the effectiveness of the method. The results show that the method proposed in this paper can significantly improve the quality of the enhanced image, and by combining it with other image contrast enhancement methods, the final enhancement result can even be better than the reference image in contrast and brightness when the contrast and brightness of the reference are not good.
低光照图像存在严重噪声、低亮度、低对比度等问题。在以往的研究中，已经提出了许多图像增强方法，但很少有方法能同时处理这些问题。在本文中，为了同时解决这些问题，我们提出了一种低光照图像增强方法，该方法可以与监督学习以及先前基于HSV（色调、饱和度、明度）或视网膜皮层理论的图像增强方法相结合。首先，我们分析了HSV颜色空间与视网膜皮层理论之间的关系，并表明增强图像的V通道（HSV颜色空间中的V通道，等同于RGB颜色空间中的最大值通道）能够很好地表示对比度和亮度增强过程。然后，提出了一种数据驱动的条件再增强网络（记为CRENet）。该网络以低光照图像作为输入，在测试时以增强后的V通道（增强图像的V通道）作为条件，进而可以对低光照图像的对比度和亮度进行再增强，同时降低噪声和颜色失真。此外，在1080Ti GPU上处理分辨率为400*600的彩色图像需要23毫秒。最后，进行了一些对比实验来证明该方法的有效性。结果表明，本文提出的方法能够显著提高增强图像的质量，并且将其与其他图像对比度增强方法相结合时，当参考图像的对比度和亮度不佳时，最终的增强结果在对比度和亮度方面甚至可以优于参考图像。
