## 4 Discussion
Details like outlines of trees and human are unobservable in the results of HE and CLAHE, and the backgrounds are distorted. Compared with the original linear mapped image, the results of AHPBC and Reinhard are still blurred, even though there might be a great change in brightness. MSR can increase the contrast to some extent, but the effect on local detail enhancement is relatively weak. The noise in the result of LEP is obvious. It can be indicated by the comparison of the results in Figures 7 and 8 that the proposed method creates the most visually comfortable results, which reveal the details most fully. Image group Figures 9 and 10 are examples of image with blurred details. Due to low contrast and weak details in the original image, HE and CLAHE not only fail to reproduce the details, but also generate noises. Objects such as trees, buildings, and pedestrians in AHPBC's results are blurred. The results of MSR and Reinhard are too dark to observe the information. Relatively, the results of LEP and the proposed method are visually pleasing; comparing with the results of LEP, noise in the proposed results is weaker. The results of the Tenengrad for the test images are shown in the Table 2. In theory, the higher value of Tenengrad, the clearer the entire image. In accordance with the result of visual comparisons, the proposed method and LEP achieve higher Tenengrads. As being reported in Table 3, comparing about the entropy, the proposed method and LEP have the robust result, and our method obtains the slightly better value than LEP does. Practically, there are more details in the results of our proposed method. As being reported in Table 4, comparing about the NIQE, lower value of NIQE reflect better perceptual quality of image. the overall difference of AHPBC, MSR, Reinhard, LEP, and our proposed method is not obvious. As being reported in Table 5, lower value of NIQE reflect better perceptual quality of image. In general, the proposed method and LEP achieve better results, but the average result of our proposed method is the best. As being reported in Table 6, as our approach introduce multiscale analysis and image fusion, the calculation time of the proposed algorithm is much more than the conventional and famous methods HE, CLAHE, MSR, and Reinhard. Our method runs relatively slower than LEP, but more quickly than AHPBC. How to accelerate our algorithm is one of the key points of our future work. Hardware acceleration is one of our choices. After optimization, our method is very likely to process image on real-time application. All in all, performance of the proposed algorithm is verified by experiments with images with various characteristics. The above analysis of the results shows that the proposed method has strength in detail enhancement of the HDR infrared image. The dynamic range compression and detail enhancement results are visually comfortable without excessively obvious noise. Figure 7: Comparison Results 4 (Foggy Image). (**a**) Linear Mapping. (**b**) HE. (**c**) CLAHE. (**d**) AHPRC. (**e**) MSR. (**f**) Reinhard. (**g**) LEP. (**h**) Proposed result. Figure 8: Comparison Results 5 (Foggy Image). (**a**) Linear Mapping. (**b**) HE. (**c**) CLAHE. (**d**) AHPRC. (**e**) MSR. (**f**) Reinhard. (**g**) LEP. (**h**) Proposed result. Figure 9: Comparison Results 6 (Image with Blurred Details). (**a**) Linear Mapping. (**b**) HE. (**c**) CLAHE. (**d**) AHPBC. (**e**) MSR. (**f**) Reinhard. (**g**) LEP. (**h**) Proposed result. Figure 10: Comparison Results 7 (Image with Blurred Details). (**a**) Linear Mapping. (**b**) HE. (**c**) CLAHE. (**d**) AHPBC. (**e**) MSR. (**f**) Reinhard. (**g**) LEP. (**h**) Proposed result.