# Improved Retinex-Theory-Based Low-Light Image Enhancement Algorithm
## 4 Experiments
### Experimental Comparison  
We compare the methods in this paper visually and objectively with traditional enhancement methods and the latest enhancement methods. Figures 7-9 show the comparison results of the aforementioned methods on the LOL dataset, VE-LOL-H dataset [24], and LIME dataset [25]. Figure 7 illustrates the effect of different methods on the enhancement of images from the LOL dataset. The original input image in low illumination has very low brightness and significant loss of image information. From Figure 7, it is apparent that some of the methods, such as LightenNet, RRDNet, and DSLR, do not show a significant enhancement effect since the enhanced image is not fully exposed, and the enhancement effect could be better. The color of the images from Retinex, RetinexNet, MBLLEN, and EnlightenGAN is distorted, especially in the case of EnlightenGAN in which the color of the leaves turns yellow, and the color deviation is significant. In contrast, the proposed method was able to enhance the image quality significantly. It also preserved the details of the image. Figure 8 illustrates the enhancement of indoor images from the VE-LOL-H dataset by different methods. The brightness enhancement of the two conventional methods, Retinex and MSRCP, is more pronounced in Figure 8; however, image noise is amplified, and severe color distortion is present. The brightness enhancement effect of Zero-DCE and Zero-DCE++ is better than the method proposed in this paper, but the overall image is obviously white, and the image brightness is too saturated. Figure 9 illustrates the results of enhancing images from the LIME dataset, wherein an image with a backlighting effect was selected. The original image exhibits rich color information, while some methods introduced severe distortion in the backlight region. For instance, the Retinex effect resulted in the complete disappearance of white clouds. Furthermore, the white clouds in the background of the DSLR enhancement were considerably attenuated, RetinexNet and MSRCP resulted in a noticeable amplification of noise, and the enhanced images of Zero-DCE and Zero-DCE++ exhibited excessive whiteness and severe color deviation. In contrast to the aforementioned methods, the proposed approach achieved a more balanced enhancement between color brightness and contrast. This study compares four image datasets, including the LOL [5], LIME [25], VV, and DICM [26] datasets. To ensure a fair comparison, we selected publicly available codes without any modifications, and training datasets were also sourced from the LOL dataset. For methods that use unpaired images for training, such as EnlightenGAN and Zero-DCE, we compared them using pre-trained models provided by their respective authors. We evaluated the LOL dataset's test set using objective evaluation metrics, such as PSNR, SSIM, MSE, and LPIPS. The specific results are shown in Table 1. The LIME, VV, and DICM datasets only contain low-light images. We evaluated the image quality of these datasets using NIQE, and the specific results are shown in Table 2.  
Figure 7: The results of a low-light image enhancement procedure on the LOL dataset were obtained. (**a**) Low-illumination image; (**b**) Retinex; (**c**) MSRCP; (**d**) MBLLEN; (**e**) LightenNet; (**f**) RetinexNet; (**g**) EnlightenGAN; (**h**) RRDNet; (**i**) R2 FNet; (**j**) DSLR; (**k**) ExCNet; (**l**) Zero-DCE; (**m**) Zero-ED++; (**n**) our method; (**o**) Truth value. Figure 8: The results of a low-light image enhancement procedure on the VE-LOL-H dataset were obtained. (**a**) Low-illumination image; (**b**) Retinex; (**c**) MSRCP; (**d**) MBLLEN; (**e**) LightenNet; (**f**) RetinexNet; (**g**) EnlightenGAN; (**h**) RRDNet; (**i**) R2 RNet; (**j**) DSLR; (**k**) ExCNet; (**l**) Zero-DCE; (**m**) Zero-ECD++; (**n**) our method; (**o**) Truth value. Figure 9: The results of a low-light image enhancement procedure on the LIME dataset were obtained.