## 5 Results and Discussion
### Ablation Study  
To understand the contribution of each design choice, we conducted ablation experiments on the following:  
* Loss functions: Removing perceptual loss (\(\mathcal{L}_{\text{VGG}}\)) increases LPIPS by +0.021, indicating lower perceptual fidelity. Removing SSIM results in structural degradation and amplification of local artifacts.
* Attention modules: Disabling channel-wise attention (SE blocks) reduces SSIM by approximately 0.02 and leads to visibly over-smoothed textures.
* Synthetic data: Excluding synthetically generated low-light samples from training results in a PSNR drop of approximately 1.1 dB on test cases with challenging illumination conditions.  
These findings confirm that each component meaningfully contributes to the model's ability to generalize and recover image quality under low-light conditions.  
To further validate this, we present a visual ablation study in Figure 3, comparing the outputs of the full model against three ablated variants: (i) without perceptual loss, (ii) without attention modules, and (iii) without synthetic training data. The visual results demonstrate the practical effects of each modification, highlighting texture degradation, structural blur, or reduced illumination robustness in the respective variants.  
We further expand the study to include three additional low-light scenarios: (1) an indoor scene with sensor noise, (2) an outdoor scene with uneven lighting, and (3) a highly textured natural scene. Figure 4 illustrates these cases, and quantitative metrics are reported in Table 3.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline
**Variant** & **Indoor PSNR** & **Indoor SSIM** & **Outdoor PSNR** & **Outdoor SSIM** & **Textured PSNR** & **Textured SSIM** & **LPIPS (avg)** \\ \hline Full model & 28.3 & 0.875 & 26.7 & 0.861 & 27.9 & 0.867 & 0.135 \\ w/o perceptual loss & 26.9 & 0.854 & 25.3 & 0.840 & 26.5 & 0.841 & 0.154 \\ w/o attention modules & 26.5 & 0.843 & 24.8 & 0.828 & 25.7 & 0.832 & 0.166 \\ w/o synthetic data & 26.8 & 0.848 & 25.0 & 0.832 & 26.0 & 0.839 & 0.159 \\ \hline \end{tabular}
\end{table}
Table 3: Quantitative ablation results across three scene types (indoor, outdoor, and textured) using PSNR, SSIM, and LPIPS metrics. The full model consistently achieves the best performance. Removing perceptual loss or attention modules leads to notable perceptual degradation, while excluding synthetic data weakens generalization under challenging illumination conditions.  
Figure 4: Side-by-side comparison of the original high-resolution low-light images (**left**) and their corresponding 256 \(\times\) 256 cropped versions (**right**). These crops were selected to emphasize scene characteristics relevant to the ablation study: (**top**) indoor environment with dim lighting, (**middle**) outdoor scene with uneven illumination, and (**bottom**) natural scene with rich textures.  
Figure 3: Visual ablation study illustrating the impact of different components in the proposed model. The absence of perceptual loss leads to less texture fidelity, removal of attention modules results in over-smoothing, and excluding synthetic training data reduces illumination generalization. The full model consistently produces the most balanced and natural output.