A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model

作者：Shiping Ma, Hongqiang Ma, Yuelei Xu, Shuai Li, Chao Lv, Mingming Zhu · 发表于：Sensors · 年份：2018 · DOI：10.3390/s18103583 · 被引用次数：36 · 研究领域：Image Enhancement Techniques、Advanced Image Fusion Techniques、Advanced Image Processing Techniques

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 (S) and the specially designed Deep Convolutional Neural Network is applied to enhance the intensity component (I). 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.