# Low-Light Image Enhancement Method Based on Retinex Theory by Improving Illumination Map
## 4 Experiments and Results
### Objective Quality Assessments  
To quantitatively evaluate the performance of the proposed method, we performed a full-reference evaluation and a non-reference evaluation. Specifically, we used 104 images from different datasets for comparative experiments. These images were composed of two parts, a total of 13 images from Figures 10-22 and 91 other images, respectively. The average values of the corresponding quantitative indicators are shown in Table 1, and the best indicators are shown in bold. Where, (\(\uparrow\)) means that the larger the value, the better, and (\(\downarrow\)) is the opposite.  
(1) Non-Reference Evaluation: We employed five non-reference metrics, that is, NIQE, BRISQUE, CPCQI, IL-NIQE, and NIQMC, for low-light image quality evaluation.  
NIQE constructs a \"quality-aware\" collection of statistical features based on the Natural Scene Statistics (NSS) model in the spatial domain. BRISQUE uses NSS with locally normalized luminance coefficients to quantify the possible loss of \"naturalness\" in order to assess the overall image quality. CPCQI uses a regression module to generate visual quality metrics by analyzing 17 features such as contrast, sharpness, and luminance. IL-NIQE learns multivariate Gaussian models of image blocks by integrating statistical features from multiple natural images. Additionally, the Bhattacharyya-like distance is used to measure the quality of each image block, and the overall quality score is obtained by averaging pooling. NIQMC combines local and global determination of which image has greater contrast and better quality between two images. The lower the values of NIQE, BRISQUE, and IL-NIQE, the higher the image quality is, while higher scores for CPCQI and NIQMC mean that a better contrast is obtained than the reference image.  
As shown in Table 1, the proposed method had the optimal NIQE and CPCQI values, with the BRISQUE value and IL-NIQE ranking second and NIQMC ranking fourth. This  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **NIQE** & **BRISQUE** & **CPCQI** & **IL-NIQE** & **NIQMC** & **PCQI** \\  & (\(\downarrow\)) & (\(\downarrow\)) & (\(\uparrow\)) & (\(\downarrow\)) & (\(\uparrow\)) & (\(\uparrow\)) \\ \hline BIMEF [45] & 4.242 & 19.686 & 1.030 & 27.523 & 4.820 & 1.036 \\ \hline MF [27] & 4.346 & 20.181 &  
means that the results of the proposed method were more consistent with human vision, and the contrast was in the middle position.  
(2) Full-Reference Evaluation: We conducted a full-reference evaluation using the PCQI metric. PCQI decomposes the image block into mean intensity, signal intensity and signal structure components and evaluates the perceived distortion of these components separately. A higher PCQI denotes that the result has higher visual effect and quality. As shown in Table 1, our proposed method stood out as the best performer.  
This part verified the effectiveness of our method from two aspects: full reference and non-reference. Although not every index had the best results, the model outperformed other algorithms overall.