## 4 Experiment
### Subjective Evaluation  
We chose 14 popular low-light image enhancement methods to compare with our proposed method. The 14 compared methods include: Retinex-theory-based methods: SRIE [15] (2016), JED [9] (2018), SDD [39] (2020), and RetinexDIP [18] (2021); methods based on image fusion: BIMEF [14] (2017), FFM [40] (2019), and EFF [13] (2017); and deep-learning-based methods: RetinexNet [1] (2018), KinD [2] (2019), EnGAN [3] (2020), DLN [19] (2020), DRBN [21] (2020), Zero-DCE [22] (2020), and Zero-DCE++ [4] (2021). In this section, we analyze the visual effects of the different methods on different datasets. First, we compare the proposed method with the method based on Retinex theory, as shown in Figure 5. Among the methods based on Retinex theory, we chose: SRIE [15], JED [9], SDD [39], and RetinexDIP [18]. We randomly selected four low-light images from the LOL and Brightening Train test datasets to demonstrate the enhanced visual effects of the four methods, as shown in Figure 5. Figure 5a shows the input low-light images. The darker regions of the original images produced by the SRIE method are not well-exposed. As in the second column, the book bag and other objects in the first image have low luminance and cannot clearly distinguish each object. The JED method performs noise smoothing during the enhancement process, so the resulting image has less noise and uniform luminance. However, due to excessive smoothing, some texture information is lost, resulting in blurred images. For example, the outline of the building in the third image in the second row is defocused. The brightness of the image enhanced by the SDD method is not sufficiently increased, the image tones are deepened, and the details of the image are not well-recovered. Figure 5e shows the image produced by the RetinexDIP method, the overall brightness of the image is not uniform, the color information is not accurate, and the image is prone to noise. For example, in the fifth column, the color of the sky should be blue, but the color of the sky appears as gray-blue after RetinexDIP enhancement. Figure 5f shows the enhancement results of the algorithm proposed in this paper. Our proposed method improves the brightness of the image while maintaining the true color of the image, and there are no artifacts or image noise. Figure 5g shows the real label, and it can be seen that the enhancement result of our method is highly consistent with the label. In addition to comparing with the method based on Retinex theory, we also compare the proposed method with the image-fusion-based low-light image enhancement methods: BIMEF [14], FFM [40], and EFF [13]. We randomly selected six low-light images from the SICE [33] dataset, including outdoor buildings, outdoor scenery, and indoor scenes, and the results are shown in Figure 6. Figure 6a shows the original low-light images, and Figure 6b shows that the brightness of the images processed with the BIMEF method is not sufficiently enhanced, and there seems to be a layer of gray enveloping the images. Figure 6c,e shows the enhancement results of EFF and FFM, respectively. It can be observed that the brightness and sharpness of the image have been greatly improved, but some areas are still not fully exposed. As in the fourth image in the second row, the stone columns in the gallery hall are still dark. Figure 6e shows the enhancement results of our method, compared with the real label in Figure 6f. Our image quality has a substantial improvement, the darker areas of the image become uniform in brightness, and the color information of the whole image is accurate. In Figure 7, we use CNN-based low-light image enhancement algorithms to enhance images with RetinexNet [1], KinD [2], EnGAN [3], DLN [41], Zero-DCE [22], DRBN [21], and Zero-DCE++ [4]. We randomly selected six images from the MIT [32] dataset for visual effect demonstration, and the results are shown in Figure 7. From the visual effect observation, the overall quality of the enhanced images is better, but there are still some images with defects.