# Content-Adaptive Light Field Contrast Enhancement Using Focal Stack and Hierarchical Network
## 4 Experimental Results and Discussion
### Subjective Assessment  
While it would be desirable to have an objective assessment method to compare contrast enhancement techniques, unfortunately there is no accepted objective criterion in the literature that can provide meaningful results for each image [13]. On this basis, we have tried to find more suitable objective evaluation criteria to evaluate image quality. In terms of the subjective evaluation experiment, we designed a questionnaire and invited 50 people to respond to the questionnaire to evaluate the light field images obtained by the proposed method and the HE, CLAHE, and Imadjust methods. The results show that 85% of the people thought that the image quality obtained by our method was better, and 10% of the people thought that the image results obtained by the CLAHE method were better. A total of 5% considered the image quality obtained by the Imadjust method to be better. The results show that our method is more consistent with human viewing logic and has a better visual effect.  
Figure 6 shows the original LF images and their various corresponding contrast-enhanced images. We compared our method with HE [19], the contrast enhancement algorithm (Imadjust) proposed by [PERSON] et al. [46], and the classical contrast-limited adaptive histogram equalization (CLAHE) enhancement algorithm [47; 48]. In general, the HE of an image can best utilize the dynamic range of pixel values for maximum contrast. However, the generated images generally have low visual quality. This finding can also be seen in Figure 6c. The amplification of noise reduces the image quality, and undesired artifacts become more prominent, which results in an unnatural appearance. As shown in Figure 6b,d, steps were taken to ameliorate the situation, such as using the proposed  
CLAHE and Imadjust methods. The Imadjust method significantly improved the image over-enhancement, but the details were also erased. For example, the details of the bricks under cloudy conditions completely disappeared. We found that in many of the tested images, both CLAHE and our method showed similar quality and were close to the real sensation. However, our scheme yielded better overall contrast, whereas the CLAHE method had a light image color and a poor contrast effect. In addition, there were some problems, such as unnatural color transition, seen in the flowers and leaves in Figure 6b under cloudy conditions. Figure 7 illustrates the FS for contrast enhancement. Since our method was implemented based on HE, we compared it with the HE method. The contrast between the two was effectively enhanced (especially HE), and hidden details (such as stone texture) were revealed.  
Figure 6: Comparison of focal stack results. (**a**) Original image; (**b**) CLAHE method; (**c**) HE method; (**d**) Imadjust method; (**e**) our method.  
Figure 7: Focal stack comparison. **(a)** Original focal stack; **(b)** HE focal stack; **(c)** our focal stack.  
However, the HE algorithm only considers the image contrast, which causes excessive enhancement and drowns out some details of the image. On the contrary, the contrast of the image after our processing was effectively enhanced without changing the naturalness, such as the texture of the car tire and the details of the wall, which completely disappeared after HE. However, our method enhanced the contrast while enhancing the information expression and deepening the details of the original image.