## 6 Conclusions and Future Work  
In this paper, we proposed a lightweight, attention-augmented deep learning model for low-light image enhancement, designed for real-time performance and resource-constrained deployment. Our model integrates a UNet-inspired architecture with channel attention modules and is trained using a composite loss function that balances pixel-level accuracy, structural similarity, and perceptual quality.  
To improve generalization and reduce reliance on manually collected paired data, we constructed a hybrid training dataset that combines real-world low-light images with synthetically generated samples based on gamma correction and noise modeling. Through comprehensive experiments on public benchmarks such as LOL and SID, we demonstrated that our approach achieves competitive or superior performance compared to several state-of-the-art methods--while maintaining low computational complexity.  
In addition to strong PSNR, SSIM, and LPIPS scores, our model demonstrates real-time inference capabilities on both desktop GPUs and edge devices such as the Jetson Nano. These characteristics support deployment in a variety of real-world applications, including low-light video enhancement on smartphones, nighttime pedestrian detection in automotive systems, and intelligent surveillance platforms requiring real-time visibility improvements under low-light or infrared conditions. Additionally, our approach may serve as a foundation for domain-specific extensions, such as underwater visual perception, where low illumination and color distortion pose compounding challenges [3; 4; 5].  
Future work will explore several directions:  
* Video enhancement: Extending the model to handle temporal consistency and frame-level stability in low-light video sequences.
* Raw image input: Adapting the network to process raw sensor data directly for better low-light fidelity.
* Hardware-specific optimization: Applying neural architecture search and pruning techniques to further reduce latency on edge accelerators (e.g., ARM, Jetson, mobile NPUs).
* Task-aware enhancement: Integrating enhancement with downstream tasks like object detection and semantic segmentation in dark scenes.  
This work represents a practical step forward in bridging high-quality enhancement with efficient deployment.  
While our model demonstrates strong generalization and visual quality across diverse low-light conditions, certain limitations remain. Specifically, enhancement may inadvertently amplify artifacts (e.g., compression noise or motion blur) in extremely degraded inputs. Moreover, in high-stakes applications such as surveillance, security, or forensic analysis, enhanced images may alter perceived object boundaries, facial features, or scene lighting in ways that could mislead human interpretation or downstream vision models. We emphasize that enhanced images should not be treated as original evidence in such contexts. Future work could explore uncertainty-aware enhancement and trust calibration techniques to mitigate these risks. Furthermore, although our model enhances perceptual quality, the added complexity from perceptual and attention modules introduces a small computational overhead compared to some minimalist baselines--a design trade-off chosen deliberately to optimize perceptual realism. Finally, in forensic or high-stakes domains, users should be aware that enhancement can alter pixel-level properties and should not substitute raw data for evidentiary purposes.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the author. The data are not publicly available due to because the data are part of an ongoing research project and will be made available after completion of related studies.  
**Conflicts of Interest:** The author declares no conflicts of interest.