Aeronautics Engineering College, Air Force Engineering University, Xi'an 710038, China.
Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China.
Sensors (Basel). 2018 Oct 22;18(10):3583. doi: 10.3390/s18103583.
Images captured by sensors in unpleasant environment like low illumination condition are usually degraded, which means low visibility, low brightness, and low contrast. In order to improve this kind of images, in this paper, a low-light sensor image enhancement algorithm based on HSI color model is proposed. At first, we propose a dataset generation method based on the Retinex model to overcome the shortage of sample data. Then, the original low-light image is transformed from RGB to HSI color space. The segmentation exponential method is used to process the saturation () and the specially designed Deep Convolutional Neural Network is applied to enhance the intensity component (). At the end, we back into the original RGB space to get the final improved image. Experimental results show that the proposed algorithm not only enhances the image brightness and contrast significantly, but also avoids color distortion and over-enhancement in comparison with some other state-of-the-art research papers. So, it effectively improves the quality of sensor images.
传感器在低光照等不理想环境下捕捉到的图像通常会退化，表现为低可见度、低亮度和低对比度。为了改善这类图像，本文提出了一种基于 HSI 颜色模型的低光照传感器图像增强算法。首先，我们提出了一种基于 Retinex 模型的数据集生成方法，以克服样本数据不足的问题。然后，将原始低光照图像从 RGB 颜色空间转换到 HSI 颜色空间。使用分段指数方法处理饱和度分量（），并应用专门设计的深度卷积神经网络增强强度分量（）。最后，我们将结果转换回原始 RGB 空间，得到最终的增强图像。实验结果表明，与一些其他最新研究相比，所提出的算法不仅显著提高了图像的亮度和对比度，而且避免了颜色失真和过增强，从而有效提高了传感器图像的质量。
