# Enhancing Underwater Images via Color Correction and Multiscale Fusion
## 4 Results
### Detail Analysis  
The information hidden in the details of an image is often critical, and bright image details are vital for practical applications such as underwater target detection [39] and  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Resolution** & **ARC** & **Fusion** & **GDCP** & **IBLA** & **TS** & **UDCP** & **MLLE** & **ACCDO** & **OUR** \\ \hline
500 \(\times\) 375 & 1.027 & 1.499 & 0.276 & 11.169 & 0.128 & 6.664 & 0.159 & 0.488 & 0.121 \\
640 \(\times\) 480 & 1.678 & 2.476 & 0.285 & 21.696 & 0.178 & 12.630 & 0.226 & 0.811 & 0.175 \\
850 \(\times\) 564 & 2.645 & 3.833 & 0.373 & 35.711 & 0.254 & 18.946 & 0.362 & 1.251 & 0.297 \\
1280 \(\times\) 720 & 5.060 & 7.576 & 0.551 & 62.228 & 0.474 & 40.256 & 0.823 & 2.195 & 0.558 \\ Ave & 2.603 & 3.846 & 0.396 & 32.701 & **0.259** & 19.624 & 0.393 & 1.186 & 0.288 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of runtime for different methods. The red value represents the best, while the blue value represents the second best.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**UIEBR** & **ARC** & **Fusion** & **GDCP** & **IBLA** & **TS** & **UDCP** & **MLLE** & **ACCDO** & **OUR** \\ \hline AG & 4.835 & 6.319 & 7.221 & 5.989 & 7.194 & 5.207 & **12.913** & 9.522 & 12.663 \\ IE & 7.187 & 7.413 & 7.316 & 7.267 & 7.253 & 6.557 & 7.580 & 7.662 & **7.727** \\ PCQI & 1.013 & 1.066 & 1.045 & 1.074 & 1.148 & 0.814 & **1.221** & 1.191 & 1.044 \\ UIQM & 3.214 & 3.516 & 2.568 & 2.560 & 3.245 & 2.405 & 2.607 & 3.520 & **3.745** \\ UCIQE & 0.563 & 0.588 & 0.610 & 0.604 & 0.601 & 0.584 & 0.605 & 0.555 & **0.620** \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of indicators for different methods in UIEBR. The red value represents the best, while the blue value represents the second best.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**UIEBC** & **ARC** & **Fusion** & **GDCP** & **IBLA** & **TS** & **UDCP** & **MLLE** & **ACCDO** & **OUR** \\ \hline AG & 3.139 & 4.512 & 4.848 & 4.302 & 5.070 & 3.055 & **7.734** & 6.512 & 6.347 \\ IE & 7.057 & 7.253 & 7.122 & 6.998 & 7.215 & 5.638 & 7.312 & 7.519 & **7.624** \\ PCQI & 0.992 & 0.998 & 0.954 & 1.011 & 1.052 & 0.801 & **1.086** & 1.071 & 0.914 \\ UIQM & 2.149 & 2.175 & 1.882 & 1.841 & 2.386 & 1.621 & 1.648 & 1.952 & **2.214** \\ UCIQE & 0.536 & 0.572 & 0.565 & 0.591 & 0.574 & 0.520 & 0.579 & 0.549 & **0.596** \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of indicators for different methods in UIEBC. The red value represents the best, while the blue value represents the second best.  
tracking. Therefore, an excellent underwater image-enhancement algorithm should correct the color, improve contrast, and enhance image details. Figure 6 shows the enhanced images and the details produced by different algorithms. The MLLE and ACCDO algorithms have achieved satisfactory results. ARC, Fusion, and TS algorithms enhance image details to a certain extent. The pattern of the small fish in Figure 6 is clearer than in the original image, but the contrast is somewhat lacking; GDCP and IBLA have improved contrast, but there is no enhancement of details. The algorithm in this article takes into account both details and contrast.