光学学报, 2019, 39 (2): 0210004, 网络出版: 2019-05-10   

基于深度卷积神经网络的低照度图像增强 下载: 3308次

Low-Light Image Enhancement Based on Deep Convolutional Neural Network
作者单位
1 空军工程大学航空工程学院, 陕西 西安 710038
2 西北工业大学无人系统技术研究院, 陕西 西安 710072
摘要
针对低照度条件下图像降质严重的问题,提出了一种基于深度卷积神经网络(DCNN)的低照度图像增强算法。该算法根据Retinex模型合成训练样本,将原始低照度图像从RGB (Red Green Blue)空间转换到HSI (Hue Saturation Intensity)颜色空间,保持色度分量和饱和度分量不变,利用DCNN对亮度分量进行增强,最后将HSI颜色空间转换到RGB空间,得到最终的增强图像。实验结果表明,与现有主流的图像增强算法相比,所提算法不仅能够有效提升亮度和对比度,改善过增强现象,而且能够避免色彩失真,主观视觉和客观评价指标均得到了进一步提高。
Abstract
Aim

ing at the problem of the severe image degradation under a low-light condition, a low-light image enhancement algorithm based on deep convolutional neural network (DCNN) is proposed. The training sample is synthesized by this algorithm according to the Retinex model. Then, the original low-light image is converted from RGB (Red Green Blue) space to HSI (Hue Saturation Intensity) color space. The luminance component is enhanced by using the DCNN while keeping the chrominance component and the saturation component unchanged. Finally, the image is turned back to the RGB space from HSI color space to get the finally enhanced image. The experimental results show that, compared with the existing excellent image enhancement algorithms, the proposed algorithm can not only effectively enhance the brightness and the contrast, but also can avoid the color distortion and the over-enhancement, and both the subjective vision and objective evaluation index are further improved.

马红强, 马时平, 许悦雷, 朱明明. 基于深度卷积神经网络的低照度图像增强[J]. 光学学报, 2019, 39(2): 0210004. Hongqiang Ma, Shiping Ma, Yuelei Xu, Mingming Zhu. Low-Light Image Enhancement Based on Deep Convolutional Neural Network[J]. Acta Optica Sinica, 2019, 39(2): 0210004.

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