# Enhancement of Low-Light Images Using Illumination Estimate and Local Steering Kernel
## 4 Simulation and Results
### Experimental Result and Visual Compare  
Figures 2-5 show the low-light image and the enhanced and enlarged images, where (a) is the low-light image, (b-e) are the results of processing with existing techniques, namely, dark light [23], LIME [8], RBMP [24], and LIIEN [4], respectively, and (f) shows the result of processing with the proposed technique.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Variable** & **Value** \\ \hline Gamma weight parameter & \(\tau\) & \(0.45\) \\ Color compensation weight & \(\alpha\) & \(1\) \\ Smoothing parameter & \(h\) & \(1.5\) \\ Steering kernel size & \(K_{x,y}\) & \(15\times 15\) \\ Window size & \(w\) & \(3\times 3\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter set of proposed image enhancement method.  
Figure 3: Enhancement result and enlarged image of low-light image (LOL547). (**a**) Low-light image, (**b**) dark light, (**c**) LIME, (**d**) RBMP, (**e**) LIIEN, and (**f**) proposed method.  
Figure 2: Enhancement result and enlarged image of low-light image (LOL493). (**a**) Low-light image, (**b**) dark light, (**c**) LIME, (**d**) RBMP, (**e**) LIIEN, and (**f**) proposed method.  
In Figure 2, we compare object detection performance of very dark images. As a result, the detection performance of the images processed with dark light and LIME is insufficient.  
Figure 4: Enhancement result and enlarged image of low-light image (LOL65). (**a**) Low-light image, (**b**) dark light, (**c**) LIME, (**d**) RBMP, (**e**) LIIEN, and (**f**) proposed methods.  
Figure 5: Enhancement result and enlarged image of low-light image (LOL780). (**a**) Low-light image, (**b**) dark light, (**c**) LIME, (**d**) RBMP, (**e**) LIIEN, and (**f**) proposed method.  
The RBMP-processed footage greatly improves the illumination of the footage, but when the dark areas are zoomed in, a significant presence of green light is noticed. The proposed technique sufficiently improves the illumination of the image, and the visibility of the dark areas is naturally enhanced.  
In Figure 3, the dark light and LIME treatments do not improve the illumination of the background areas. RBMP's enlargements are less visible due to the lack of contrast in the color tones, and LIIEN's enlargements show relatively much noise. The proposed algorithm performs well in improving the overall illumination, and the color contrast of the enlarged image is clearly visible.  
In Figure 4, the images processed with dark light and RBMP have low color contrast, resulting in blurry enlargements. The results of LIME and LIIEN show good overall visibility with sharp color contrast, but not enough illumination enhancement in dark areas. Overall, the proposed algorithm shows good visibility of objects in dark areas and clear color contrast.  
In Figure 5, we zoom in on the areas where dark and light areas appear together, and the images processed with LIME and LIIEN appear darker in low-light areas and distorted in bright areas. The images processed with dark light and RBMP show a significant improvement in the dark areas, but the resulting images have a full color contrast. The images processed by the proposed algorithm do not show any color distortion, and the overall result has vivid tones and good visibility.  
Visual evaluation shows that the resulting images from existing methods have inadequate illumination enhancement or color distortion. On the other hand, the resulting image of the proposed algorithm exhibits sufficiently improved brightness, sharp contrast of colors, and natural texture of the image.