# A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model
## 4 Experiments Results
### Comparison with State-of-the-Art Methods
#### 4.4.2 Evaluation on Real-Word Dataset  
In order to show the proposed algorithm capability for the enhancement of not only the synthetic low-light images but also for actual low-light images, 17 typical images were selected from NASA [32], DICM [33] and VV [34] dataset. All chosen images are real low-light images taken by commercial sensor cameras. The experimental results are shown in Figure 9 and written in Table 3.  
For subjective evaluation, in Figure 9, the four enhanced images listed as (a)-(e) from top to bottom are shown. Images a,c,d show that, the HE algorithm can improve the overall brightness and contrast, but serious color deviation occurs due to the super saturation. The image color is not changed \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **HE** & **Dong** & **SRIE** & **LIME** & **Ours** \\ \hline PSNR & 16.19 & 16.29 & 21.08 & 13.47 & 21.33 \\ SSIM & 0.7985 & 0.7947 & 0.9579 & 0.8097 & 0.9204 \\ MSE & 1928.3 & 1699.6 & 686.4 & 3230.7 & 389 \\ LOE & 505 & 2040 & 776 & 1277 & 402 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Objective evaluation of different algorithms for synthetic low-light images. Figure 9: The subjective visual comparison of different algorithms for real low-light images. (**a**) the imae from DICM, (**b**) The image from VV dataset, (**c**) The image from NASA dataset, (**d**) The image from NASA dataset, (**e**) the result of partial enlargement of (**c**). by Dong algorithm, but serious edge effect has occurred. Because, Dong algorithm uses defogging method for image enhancement, where the low-light image after inversion is not exactly the same as a foggy image. Therefore, Dong algorithm is not to able to estimate the atmospheric light value of pseudo-foggy images. Moreover, SRIE algorithm is appropriate for slightly dark regions, and it is not recommended for the brightness improvement of dark regions where the details cannot be easily distinguished. Although LIME algorithm improves the brightness of dark regions, its main drawbacks are over-enhancement and color distortion [35; 36]. As shown in Figure 9a, the leaves and the stone tablet have the color distortion. In addition, the right half face of the girl in Figure 9e is originally exposed. LIME algorithm takes the same degree of enhancement whether the region in the original low-light image is normal or dark. So we can see that the right half of the face has been excessively enhanced, which is not consistent with human's visual perception. Our proposed algorithm can well maintain the color and avoid the over-enhancement, but it is really not as good as the LIME algorithm in enhancing brightness, especially for a particularly dark area. Obviously, there is no ground truth or reference for the real low-light images, that means the objective quality evaluation with no reference. In this paper, information entropy, color change degree, and LOE are used to evaluate the enhanced images in terms of objective criteria. The information entropy indicates the scale of the image information, the large value shows rich image with more details. The degree of changing hue in the enhanced image reflects the color preservation, i.e., the small value indicates the low color distortion. VIF is an image quality evaluation index which was proposed based on the natural image statistical model, image distortion model, and human visual system model. The greater the value of VIF, the better the image quality is [37; 38]. Table 3 gives the average values of various objective evaluation indexes for enhancing 17 low-light images by different algorithms. According to the results written in Table 3, HE and Dong have higher color change values, they have the worst capacity for color retention. LIME has higher information entropy which shows that the algorithm preserves the image information as well, but the corresponding LOE value is large, that indicates the order of image brightness has been destroyed and the naturalness is poorly retained. Moreover, SRIE algorithm has the lowest information entropy.