## 4 Discussion  
Image group Figure 4 is an example of infrared images of rich scene information including human, bicycles, benches, ground, and so on. HE and CLAHE enhanced the contrast, while a large amount of local details lost. The AHPBC and MSR can enhance the details to some extent, while the dynamic range of the result image is so small that the visibility is poor. The result of Reinhard is visually comfortable but the some texture information is still ambiguous. LEP can enhance the image well in general, but generates a halo. Compared with other six approaches, our method meets the best performance. Image group Figures 5 and 6 are examples of low contrast image, which contains many details about the texture. The dynamic range of the original IR image is so narrow that HE and CLAHE fail in the enhancement of the details, while some regions in their results are over-enhanced, and some noises are generated. AHPBC, MSR, and Reinhard can preserve the global contrast but has a relatively weak compatibility in the enhancement of the local details. LEP can successfully enhance the edge of the humans in the image, but some tiny details like the texture of the road are still dim. Our method yields the best enhancement results, producing global detail enhancement without noise generation. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Image** & **HE** & **CLAHE** & **AHPBC** & **MSR** & **Reinhard** & **LEP** & **Proposed** \\ \hline IM1 & 0.1140 & 0.1999 & 27.7791 & 0.9225 & 0.0257 & 0.8482 & 2.5096 \\ IM2 & 0.1195 & 0.1439 & 26.8293 & 0.1770 & 0.0284 & 1.0943 & 2.4500 \\ IM3 & 0.1111 & 0.1455 & 30.8719 & 0.1070 & 0.2198 & 1.1543 & 2.6872 \\ IM4 & 0.1158 & 0.1387 & 30.6425 & 0.1040 & 0.0202 & 1.1325 & 2.7946 \\ IM5 & 0.1253 & 0.1422 & 40.4624 & 0.0990 & 0.0207 & 1.0314 & 2.6288 \\ IM6 & 0.1112 & 0.1434 & 28.5309 & 0.0981 & 0.0208 & 1.2840 & 2.5608 \\ IM7 & 0.1463 & 0.1869 & 27.4465 & 0.9937 & 0.0203 & 2.1250 & 2.7588 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Running time of the test images. Unit: second. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Image** & **Linear Mapping** & **HE** & **CLAHE** & **AHPBC** & **MSR** & **Reinhard** & **LEP** & **Proposed** \\ \hline IM1 & 2.4030 & 7.6034 & 4.7557 & 2.3148 & 2.3713 & 2.1673 & 2.5625 & 2.8865 \\ IM2 & 3.1617 & 8.7744 & 5.8055 & 3.3352 & 3.2184 & 3.2078 & 3.4816 & 4.0588 \\ IM3 & 3.3359 & 11.5356 & 7.4972 & 3.8602 & 3.3382 & 3.2956 & 3.9761 & 4.3825 \\ IM4 & 7.5140 & 11.9366 & 7.5140 & 4.2840 & 4.8551 & 4.7213 & 4.2182 & 4.8106 \\ IM5 & 3.5719 & 15.0618 & 7.2863 & 3.4961 & 3.6931 & 3.6239 & 3.6369 & 3.4917 \\ IM6 & 4.9581 & 13.3283 & 7.4248 & 4.9065 & 4.8822 & 4.5561 & 3.9335 & 4.0222 \\ IM7 & 3.9773 & 11.7403 & 7.9685 & 3.7186 & 3.9794 & 3.8734 & 3.5809 & 3.5237 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The Naturalness Image Quality Evaluator (NIQE) values of the images. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Image** & **Linear Mapping** & **HE** & **CLAHE** & **AHPBC** & **MSR** & **Reinhard** & **LEP** & **Proposed** \\ \hline IM1 & 33.7017 & 65.3970 & 59.9335 & 33.43665 & 33.3354 & 21.2885 & 22.8901 & 24.9244 \\ IM2 & 30.8086 & 76.7303 & 74.3567 & 32.4350 & 32.8172 & 34.5472 & 20.3241 & 22.4031 \\ IM3 & 16.0431 & 82.7883 & 79.3746 & 16  
Figure 4: Comparison results 1 (Image of Complex Scene). (**a**) Linear Mapping. (**b**) histogram equalization (HE). (**c**) contrast-limited adaptive histogram equalization (CLAHE). (**d**) AHPBC. (**e**) MSR. (**f**) Reinhard. (**g**) LEP. (**h**) Proposed result. Figure 5: Comparison Results 2 (Low Contrast Image). (**a**) Linear Mapping. (**b**) HE. (**c**) CLAHE. (**d**) AHPBC. (**e**) MSR. (**f**) Reinhard. (**g**) LEP. (**h**) Proposed result. Figure 6: Comparison Results 3 (Low Contrast Image). (**a**) Linear Mapping. (**b**) HE. (**c**) CLAHE. (**d**) AHPBC. (**e**) MSR. (**f**) Reinhard. (**g**) LEP. (**h**) Proposed result. Image group Figures 7 and 8 are examples of foggy images.