# Joint Luminance Adjustment and Color Correction for Low-Light Image Enhancement Network
## 4 Experiments
### Ablation Experiments  
#### 4.5.1 Effectiveness Analysis of Luminance Adjustment Module  
First, we conducted ablations on the components of the Luminance Adjustment Module architecture. In actual low-light shooting environments, there are situations where the overall brightness is low, and there are also low-light environments with local highlights. Therefore, to enhance low-light images, we must not only improve the overall brightness of the image but also avoid improper adjustment of local areas when the brightness is enhanced, resulting in local over-brightness and thus affecting the overall visual performance of the image. The Luminance Adjustment Module mentioned in this paper considers the demand for overall brightness enhancement and an adjustment for uneven brightness. In order to verify the effectiveness of the Luminance Adjustment Module, different contrast networks were constructed to perform ablation experiments as follows: (a) LACCNet (w/o LAM) indicates a network in which the entire Luminance Adjustment Module is removed, and other structures remain unchanged; (b) LACCNet (w/o ALAM) involves removing the window attention adjustment component in the Luminance Adjustment Module and keeping other structures unchanged; (c) LACCNet (w/o CLAM) involves removing the convolution adjustment component in the Luminance Adjustment Module, while other structures remain unchanged; and (d) LACCNet represents the complete network structure.  
Figure 7 compares the enhancement effects of these four network structures on one example image in the LOL-V1 dataset. It can be seen from the table that the network with the Luminance Adjustment Module has the best lighting enhancement effect, and there is no image overexposure or over-darkness.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**LOL-V2-real**} & \multicolumn{3}{c}{**LOL-V2-syn**} & \multicolumn{3}{c}{**LSRW**} \\ \cline{2-9}  & **PSNR** & **SSIM** & **LPIPS** & **PSNR** & **SSIM** & **LPIPS** & **PSNR** & **SSIM** & **LPIPS** \\ \hline Retinexformer & 22.800 & 0.840 & 0.288 & 25.670 & 0.930 & 0.105 & 17.779 & 0.537 & 0.407 \\ SMG & 24.620 & 0.867 & 0.293 & 25.620 & 0.905 & 0.156 & 18.081 & 0.636 & 0.384 \\ LEDNet & 18.752 & 0.783 & 0.392 & 18.612 & 0.811 & 0.258 & 16.508 & 0.521 & 0.424 \\ SNR & 21.480 & 0.849 & 0.261 & 24.140 & 0.928 & 0.087 & 17.653 & 0.579 & 0.496 \\ Zero-DCE & 18.058 & 0.705 & 0.352 & 17.756 & 0.845 & 0.178 & 15.857 & 0.472 & 0.417 \\ EnlightenGAN & 18.684 & 0.740 & 0.368 & 16.486 & 0.811 & 0.226 & 17.592 & 0.508 & 0.404 \\ LACCNet (Ours) & 24.711 & 0.869 & 0.259 & 24.921 & 0.869 & 0.198 & 20.087 & 0.672 & 0.359 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison results of PSNR, SSIM, and LPIPS indicators with other SOTA methods on LOL-V2-real, LOL-V2-syn, and LSRW datasets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**LOL-Blur**} & \multirow{2}{*}{**LOL-V1**} & \multicolumn{2}{c}{**LOL-V2-**} & \multicolumn{2}{c}{**LOL-V2-**} \\  & & & **real** & **syn** & **LSRW** & **DICM** \\ \hline Retinexformer & 4.715 & 3.489 & 3.966 & 4.022 & 3.481 & 3.686 \\ SMG & 7.285 & 6.131 & 5.858 & 6.123 & 6.156 & 6.139 \\ LEDNet & 4.764 & 5.491 & 5.358 & 5.093 & 4.832 & 4.789 \\ SNR & 8.241 & 5.217 & 4.638 & 4.129 & 7.215 & 4.643 \\ Zero-DCE & 5.088 & 7.496 & 7.666 & 4.392 & 3.698 & 3.954 \\ EnlightenGAN & 4.779 & 4.778 & 5.156 & 4.073 & 3.320 & 3.570 \\ Ours & 4.685 & 4.897 & 3.912 & 3.986 & 3.294 & 3.589 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results of NIQE with other SOTA methods on 6 data sets.