# A Low-Brightness Image Enhancement Algorithm Based on Multi-Scale Fusion
## 4 Experiment and Analysis
### Objective Assessment of Image Quality
#### 4.2.3 Natural Image Quality Evaluator  
The natural image quality evaluator (NIQE) is an evaluation model that is computed from a large number of natural scene images and used to evaluate image quality. A lower NIQE value indicates better image quality. From Table 3, our enhanced images have the lowest NIQE values, indicating the best image quality.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Input** & **Dong** & **LIME** & **MSR** & **MF** & **NPE** & **SRIE** & **Ours** \\ \hline bookshelf & 961.8 & 1063.3 & 1942.6 & 563.3 & 810.3 & 608.2 & **358.2** \\ cupboard & **306.9** & 431.4 & 930.6 & 368.4 & 678.3 & 348.9 & 361.9 \\ doll & 449.9 & 645.1 & 1534.8 & 397.2 & 800.5 & 476.8 & **365.5** \\ classroom & 470.6 & 687.7 & 1636.5 & 598.1 & 1120.9 & 539.7 & **410.3** \\ swimming pool & 1244.6 & 538.2 & 1373.6 & 684.5 & 1875.2 & 1140.9 & **295.5** \\ hall & 805.6 & 622.1 & 1638.9 & 658.9 & 1698.9 & 801.1 & **280.7** \\ gym & 625.8 & 846.9 & 1742.8 & 533.3 & 1014.2 & 666.2 & **392.1** \\ wardrobe & 574.3 & 1211.5 & 2477.9 & 613.3 & 554.2 & 429.5 & **455.2** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative measurement results of LOE. The bold numbers indicate the best performance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Input** & **Dong** & **LIME** & **MSR** & **MF** & **NPE** & **SRIE** & **Ours** \\ \hline bookshelf & 9.734 & **18.763** & 16.214 & 7.199 & 8.374 & 4.908 & 11.533 \\ cupboard & 35.375 & 82.298 & **89.709** & 42.794 & 54.187 & 14.187 & 63.57 \\ doll & 41.627 & 146.493 & **165.138** & 59.936 & 63.301 & 16.742 & 66.871 \\ classroom & 42.835 & 125.786 & **137.805** & 55.984 & 85.473 & 16.337 & 95.942 \\ swimming pool & 9.256 & **23.655** & 19.558 & 9.109 & 13.393 & 4.373 & 15.948 \\ hall & 8.537 & **18.114** & 14.541 & 8.858 & 9.876 & 4.361 & 10.375 \\ gym & 23.621 & **84.685** & 70.673 & 30.145 & 48.797 & 9.566 & 54.606 \\ wardrobe & 22.204 & **42.362** & 36.703 & 16.675 & 22.063 & 9.233 & 31.02 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative measurement results of VIF. The bold numbers indicate the best performance.  
The NIQE values of the algorithms were compared, and the average values of the three datasets were calculated by running experiments on each algorithm on the VV, LIME [14], and DICM [30] datasets. As can be seen from Table 4, the NIQE value obtained by our method is the smallest, which proves the superiority of our method.  
We comprehensively compare different evaluation metrics. This provides a more stable evaluation of the performance of the enhancement method. The most important metric for evaluating the quality of image enhancement algorithms is the LOE. As shown in Table 1, the MF algorithm produces images with a low LOE and good performance. In particular, the LOE value of the Dong algorithm is the lowest for the \"cupboard\" image, while our algorithm has the lowest LOE value for all other test images, demonstrating the superiority of our algorithm over others. Regarding the VIF indicators, the LIME and MSR algorithms show the best performance. However, subjectively, images enhanced by the LIME and MSR algorithms may suffer from brightness distortion. In contrast, our method achieves a balanced enhancement with a higher VIF value compared to other algorithms. Although our method does not achieve the highest value of VIF, it can still preserve the visual information of images well.  
When observing Tables 1-4, there are two optimal values (LOE and NIQE) in the three evaluation indexes, which indicates that the proposed method is more effective and that the enhanced image retains more detailed information while improving the brightness. Therefore, our method is better than other advanced methods.