# A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model
## 1 Introduction  
Visual information accounts for 83% of the total amount of information that is available to humans. As an extension of visual system, image sensors [1] have key role in our daily life. In general, as image sensors are high integration, low energy consumption and long life, they are widely used in consumer electronics, transportation, medical imaging, astronomical observation and military. However, the captured image quality is strongly dependent on the environment conditions. For example, images captured by sensors under low-light condition, are usually degraded that means having low brightness, low contrast, being blurred or loose the image details. The mentioned drawbacks in image sensors make it difficult for human to distinguish and recognize objects, and brings severe challenges to the subsequent high-level tasks such as target detection [2], video surveillance and so on. Although some cameras have been developed for the low-light environment, there are still some limitation in large scale usage; they are too expensive, they need strict requirements for sensors and lenses. Therefore, using and developing image processing technology for low-light sensor image improvement of the real world is obligatory. Image enhancement aims to recover real scene information under low illumination in sensor images through relevant technical means and methods, to obtain complete details, structural information and natural, clear images. Low-light image is essentially due to low illumination. In most real-world low illumination images, the uneven light distribution encounters the brighter and darker areas in the image. How to enlarge the pixel value of dark region and simultaneously keep constant thepixel value of brighter region is a challenge. For low-light sensor image improvement, many algorithms have been proposed. At present, the low-light sensor image enhancement methods are mainly grouped into three categories. The first one is methods based on histogram equalization (HE) [3] where the basic idea is to keep the relative relation between pixel values and make them obey uniform distribution. The original HE methods stretch all the gray levels of the histogram equally to improve the global contrast, later on, some methods performed the local processing. For example, the contrast-limited adaptive histogram equalization (CLAHE) [4] prevents the over-enhancement by restricting the stretch of similar gray levels in local image patches. These methods improve the low-light image and speed up the processing as well, but their drawback is color shift phenomenon. In addition, the image details is lost due to the grayscale merging. The second group are methods based on Retinex model [5], that it is currently the most popular low-light sensor image enhancement algorithm. According to the Retinex model, every captured image by sensors is composed of reflectance and illumination components. By discriminating the two components, the illumination of the original low-light image is removed and the reflection is realized for the image enhancement. Therefore, estimating the illumination component is the core step of any Retinex algorithms. The multi-scale Retinex (MSR) [6] and multi-scale Retinex with color restoration (MSRCR) [7] are two typical algorithms. For this purpose, i.e., discriminating the two components, recently, some new algorithms have been proposed. [PERSON] et al. [8] offered a weighted variational, [PERSON] et al. [9] proposed a simplified enhancement model LIME and achieved good results. LIME always needs a gamma correction for non-linearly rescaling the refined illumination map. This additional post-processing stage defects the model robustness, and causes severe color distortion in some special scenes [10]. The third group are methods based on dehazing model. [PERSON] et al. [11] found that the low-light image is similar to a hazed image after inversion, so they proposed enhancement through the dehazing model.