ALEX: Automated Low-Light Enhancement eXpert for Intelligent Security Systems Using Vision Transformer

Rahmatulloh, Alam; Haerani, Erna; Gunawan, Rohmat; Firdaus, Eryan Ahmad; Nugraha, Ghatan Fauzi

doi:10.2478/cait-2025-0038

Abstract

Closed-Circuit TeleVision (CCTV) performance in low-light conditions often results in poor image quality. This study introduces Automated Low-Light Enhancement eXpert (ALEX), a new architecture that combines ViTRA with SwinIR to improve image clarity. ALEX utilizes Relative Lighten Cross-Attention (RLCA) and Relative Position Encoding (RPE) in the HVI color space to enhance light intensity and color, followed by SwinIR for depth restoration and resolution enhancement. Evaluation on benchmark datasets like LOLv1, LOLv2, and SID shows that ALEX outperforms existing methods like HVI-CIDNet and ViTRA, yielding sharper, more natural results based on PSNR, SSIM, and other metrics. Real-world CCTV tests demonstrate that ALEX improves image quality, even with dimmed or downscaled images. While the integration of SwinIR increases complexity and inference time, ALEX proves to be an effective low-light enhancement solution, offering significant potential for intelligent surveillance systems and future real-time applications on resource-constrained devices.

References

Piza, E. L., B. C. Welsh, D. P. Farrington, A. L. Thomas. CCTV Surveillance for Crime Prevention: A 40-Year Systematic Review with Meta-Analysis. – Criminology and Public Policy, Vol. 18, 2019, No 1, pp. 135-159.
Search in Google Scholar Back to article
Denise Cuevas, Q. P., J. P. Carlo Corachea, E. B. Escabel, M. A. Bautista. Lou Effectiveness of CCTV Cameras Installation in Crime Prevention. – College of Criminology Research Journal, Vol. 7, 2016.
Search in Google Scholar Back to article
Sheela, A. J., S. Balaji, B. Balaji, U. Hemanth Kumar. A Survey on Crime Detection using CCTV Systems. – In: Proc. of 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA’23), 2023, pp. 254-261.
Search in Google Scholar Back to article
Ghari, B., A. Tourani, A. Shahbahrami, G. Gaydadjiev. Pedestrian Detection in Low-Light Conditions: A Comprehensive Survey. – Image and Vision Computing, Vol. 148, 2024, 105106.
Search in Google Scholar Back to article
Ai, S., J. Kwon. Extreme Low-Light Image Enhancement for Surveillance Cameras Using Attention U-Net. – Sensors (Switzerland), Vol. 20, 2020, No 2.
Search in Google Scholar Back to article
Ronneberger, O., P. Fischer, T. Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation. – In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, 2015, pp. 234-241.
Search in Google Scholar Back to article
Qu, J., R. W. Liu, Y. Gao, Y. Guo, F. Zhu, F.-Y. Wang. Double Domain Guided Real-Time Low-Light Image Enhancement for Ultra-High-Definition Transportation Surveillance. – IEEE Transactions on Intelligent Transportation Systems, Vol. 25, 2024, No 8, pp. 9550-9562.
Search in Google Scholar Back to article
Bhandari, A., A. Kafle, P. Dhakal, P. R. Joshi, D. B. Kshatri. Image Enhancement and Object Recognition for Night Vision Traffic Surveillance. – In: G. Ranganathan, X. Fernando, S. F., E. A. Y., Eds. Soft Computing for Security Applications. Springer Singapore, Singapore, 2022, pp. 733-748.
Search in Google Scholar Back to article
Werdiningsih, I., I. Puspitasari, R. Hendradi. Recognizing Daily Activities of Children with Autism Spectrum Disorder Using Convolutional Neural Network Based on Image Enhancement. – Cybernetics and Information Technologies, Vol. 25, 2025, No 1, pp. 78-96.
Search in Google Scholar Back to article
Jingchun, Z., G. Eg Su, M. Shahrizal Sunar. Low-Light Image Enhancement: A Comprehensive Review on Methods, Datasets, and Evaluation Metrics. – Journal of King Saud University – Computer and Information Sciences, 2024.
Search in Google Scholar Back to article
Jiang, Y., X. Gong, D. Liu, Y. Cheng, C. Fang, X. Shen, J. Yang, P. Zhou, Z. Wang. EnlightenGAN: Deep Light Enhancement Without Paired Supervision. – IEEE Transactions on Image Processing, Vol. 30, 2021, pp. 2340-2349.
Search in Google Scholar Back to article
Wang, L., L. Zhao, T. Zhong, C. Wu. Low-Light Image Enhancement Using Generative Adversarial Networks. – Scientific Reports, Vol. 14, 2024, No 1.
Search in Google Scholar Back to article
Lee, M. H., Y. H. Go, S. H. Lee, S. H. Lee. Low-Light Image Enhancement Using CycleGAN-Based Near-Infrared Image Generation and Fusion. – Mathematics, Vol. 12, 2024, No 24.
Search in Google Scholar Back to article
Tian, Z., P. Qu, J. Li, Y. Sun, G. Li, Z. Liang, W. Zhang. A Survey of Deep Learning-Based Low-Light Image Enhancement. – Sensors, Vol. 23, 2023, No 18, pp. 1-22.
Search in Google Scholar Back to article
Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, I. Polosukhin. Attention is All You Need. – Advances in Neural Information Processing Systems, Vol. 2017-Decem, 2017, No Nips, pp. 5999-6009.
Search in Google Scholar Back to article
Yan, Q., Y. Feng, C. Zhang, G. Pang, K. Shi, P. Wu, W. Dong, J. Sun, Y. Zhang. HVI: A New Color Space for Low-Light Image Enhancement. – In: Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’25), IEEE, 2025, pp. 5678-5687.
Search in Google Scholar Back to article
Darmawan, I., A. Rahmatulloh, R. Gunawan, R. Wahjoe Witjaksono, G. Fauzi Nugraha. ViTRA: Vision Transformer with Relative Position Embedding Attention for Low-Light Image Quality Improvement. – IEEE Access, Vol. 13, 2025, pp. 160588-160601.
Search in Google Scholar Back to article
Shaw, P., J. Uszkoreit, A. Vaswani. Self-Attention with Relative Position Representations. – In: Proc. of NAACL HLT 2018 – 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies – Proceedings of the Conference, Vol. 2, 2018, pp. 464-468.
Search in Google Scholar Back to article
Liang, J., J. Cao, G. Sun, K. Zhang, L. Van Gool, R. Timofte. SwinIR: Image Restoration Using Swin Transformer. – In: Proc. of IEEE/CVF International Conference on Computer Vision Workshops (ICCVW’21), IEEE, 2021, pp. 1833-1844.
Search in Google Scholar Back to article
Chen, Z., K. Pawar, M. Ekanayake, C. Pain, S. Zhong, G. F. Egan. Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging – State-of-the-Art and Challenges. – Journal of Digital Imaging, 2023, pp. 204-230.
Search in Google Scholar Back to article
Liu, Z., Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, B. Guo. Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. – In: Proc. of IEEE/CVF International Conference on Computer Vision (ICCV’21), IEEE, 2021, pp. 9992-10002.
Search in Google Scholar Back to article
Wei, C., W. Wang, W. Yang, J. Liu. Deep Retinex Decomposition for Low-Light Enhancement. – In: Proc. of British Machine Vision Conference (BMVC’18). Vol. 2019. 2018, 61772043.
Search in Google Scholar Back to article
Bogdanova, V. Image Enhancement Using Retinex Algorithms and Epitomic Representation. –Cybernetics and Information Technologies, Vol. 10, 2010, No 3, pp. 20-30.
Search in Google Scholar Back to article
Yang, W., W. Wang, H. Huang, S. Wang, J. Liu. Sparse Gradient Regularized Deep Retinex Network for Robust Low-Light Image Enhancement. – IEEE Transactions on Image Processing, Vol. 30, 2021, pp. 2072-2086.
Search in Google Scholar Back to article
Cai, J., S. Gu, L. Zhang. Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images. – IEEE Transactions on Image Processing, Vol. 27, 2018, No 4, pp. 2049-2062.
Search in Google Scholar Back to article
Chen, C., Q. Chen, J. Xu, V. Koltun. Learning to See in the Dark. – In: Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018, pp. 3291-3300.
Search in Google Scholar Back to article
Lee, C., C. Lee, C.-S. Kim. Contrast Enhancement Based on Layered Difference Representation of 2D Histograms. – IEEE Transactions on Image Processing, Vol. 22, 2013, No 12, pp. 5372-5384.
Search in Google Scholar Back to article
Guo, X., Y. Li, H. Ling. LIME: Low-Light Image Enhancement via Illumination Map Estimation. – IEEE Transactions on Image Processing, Vol. 26, 2017, No 2, pp. 982-993.
Search in Google Scholar Back to article
Ma, K., K. Zeng, Z. Wang. Perceptual Quality Assessment for Multi-Exposure Image Fusion. – IEEE Transactions on Image Processing, Vol. 24, 2015, No 11, pp. 3345-3356.
Search in Google Scholar Back to article
Wang, S., J. Zheng, H. M. Hu, B. Li. Naturalness Preserved Enhancement Algorithm for Non-Uniform Illumination Images. – IEEE Transactions on Image Processing, Vol. 22, 2013, No 9, pp. 3538-3548.
Search in Google Scholar Back to article
Vonikakis, V., R. Kouskouridas, A. Gasteratos. On the Evaluation of Illumination Compensation Algorithms. – Multimedia Tools and Applications, Vol. 77, 2018, No 8, pp. 9211-9231.
Search in Google Scholar Back to article
Wang, Z., A. C. Bovik, H. R. Sheikh, E. P. Simoncelli. Image Quality Assessment: from Error Visibility to Structural Similarity. – IEEE Transactions on Image Processing, Vol. 13, 2004, No 4, pp. 600-612.
Search in Google Scholar Back to article
Liu, R., L. Ma, J. Zhang, X. Fan, Z. Luo. Retinex-Inspired Unrolling with Cooperative Prior Architecture Search for Low-Light Image Enhancement. – In: Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’21), IEEE, 2021, pp. 10556-10565.
Search in Google Scholar Back to article
Zhang, Y., J. Zhang, X. Guo. Kindling the Darkness. – In: Proc. of 27th ACM International Conference on Multimedia, ACM, New York, NY, USA, 2019, pp. 1632-1640.
Search in Google Scholar Back to article
Guo, C., C. Li, J. Guo, C. C. Loy, J. Hou, S. Kwong, R. Cong. Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement. – In: Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’20), IEEE, 2020, pp. 1777-1786.
Search in Google Scholar Back to article
Zhang, R., P. Isola, A. A. Efros, E. Shechtman, O. Wang. The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. – In: Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018, pp. 586-595.
Search in Google Scholar Back to article
Mittal, A., A. K. Moorthy, A. C. Bovik. No-Reference Image Quality Assessment in the Spatial Domain. – IEEE Transactions on Image Processing, Vol. 21, 2012, No 12, pp. 4695-4708.
Search in Google Scholar Back to article
Zeng, H., J. Cai, L. Li, Z. Cao, L. Zhang. Learning Image-Adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-Time. – IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 2020, pp. 2058-2073.
Search in Google Scholar Back to article
Yang, W., S. Wang, Y. Fang, Y. Wang, J. Liu. From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement. – In: Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’20), IEEE, 2020, pp. 3060-3069.
Search in Google Scholar Back to article
Liu, R., L. Ma, J. Zhang, X. Fan, Z. Luo. Retinex-Inspired Unrolling with Cooperative Prior Architecture Search for Low-Light Image Enhancement. – In: Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’21), IEEE, 2021, pp. 10556-10565.
Search in Google Scholar Back to article
Zamir, S. W., A. Arora, S. Khan, M. Hayat, F. S. Khan, M.-H. Yang. Restormer: Efficient Transformer for High-Resolution Image Restoration. – In: Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’22), IEEE, 2022, pp. 5718-5729.
Search in Google Scholar Back to article
Zhou, S., C. Li, C. Change Loy. LEDNet: Joint Low-Light Enhancement and Deblurring in the Dark. – In: Lecture Notes in Computer Science Computer Vision. Springer, Nature, Switzerland, 2022, 573-589.
Search in Google Scholar Back to article
Xu, X., R. Wang, C.-W. Fu, J. Jia. SNR-Aware Low-light Image Enhancement. – In: Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’22), IEEE, 2022, pp. 17693-17703.
Search in Google Scholar Back to article
Fu, Z., Y. Yang, X. Tu, Y. Huang, X. Ding, K.-K. Ma. Learning a Simple Low-Light Image Enhancer from Paired Low-Light Instances. – In: Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’23), IEEE, 2023, pp. 22252-22261.
Search in Google Scholar Back to article
Wang, Y., R. Wan, W. Yang, H. Li, L.-P. Chau, A. Kot. Low-Light Image Enhancement with Normalizing Flow. – In: Proc. of AAAI Conference on Artificial Intelligence, Vol. 36, 2022, No 3, pp. 2604-2612.
Search in Google Scholar Back to article
Wang, T., K. Zhang, T. Shen, W. Luo, B. Stenger, T. Lu. Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method. – Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37, 2023, No 3, pp. 2654-2662.
Search in Google Scholar Back to article
Cai, Y., H. Bian, J. Lin, H. Wang, R. Timofte, Y. Zhang. Retinexformer: One-Stage Retinex-Based Transformer for Low-light Image Enhancement. – In: Proc. of IEEE International Conference on Computer Vision, 2023, pp. 12470-12479.
Search in Google Scholar Back to article
Cotogni, M., C. Cusano. TreEnhance: A Tree Search Method for Low-Light Image Enhancement. – Pattern Recognition, Vol. 136, 2023, 109249.
Search in Google Scholar Back to article
Wen, Y., P. Xu, Z. Li, W. Xu. (ATO) An Illumination-Guided Dual Attention Vision Transformer for Low-Light Image Enhancement. – Pattern Recognition, Vol. 158, 2025.
Search in Google Scholar Back to article
Pei, X., Y. Huang, W. Su, F. Zhu, Q. Liu. FFTFormer: A Spatial-Frequency Noise Aware CNN-Transformer for Low Light Image Enhancement. – Knowledge-Based Systems, Vol. 314, 2025.
Search in Google Scholar Back to article
He, M., R. Wang, M. Zhang, F. Lv, Y. Wang, F. Zhou, X. Bian. SwinLightGAN: A Study of Low-Light Image Enhancement Algorithms Using Depth Residuals and Transformer Techniques. – Scientific Reports, Vol. 15, 2025, No 1.
Search in Google Scholar Back to article
Reis, M. J. C. S. Low-Light Image Enhancement Using Deep Learning: A Lightweight Network with Synthetic and Benchmark Dataset Evaluation. – Applied Sciences (Switzerland), Vol. 15, 2025, No 11.
Search in Google Scholar Back to article
Jiang, Y., J. Zhu, L. Li, H. Ma. A Joint Network for Low-Light Image Enhancement Based on Retinex. – Cognitive Computation, Vol. 16, 2024, No 6, pp. 3241-3259.
Search in Google Scholar Back to article
Yin, M., J. Yang. ILR-Net: Low-Light Image Enhancement Network Based on the Combination of Iterative Learning Mechanism and Retinex Theory. – PLoS ONE, Vol. 20, 2025, No 2.
Search in Google Scholar Back to article
Li, R.-K., M.-H. Li, S.-Q. Chen, Y.-T. Chen, Z.-H. Xu. Dark2Light: Multi-Stage Progressive Learning Model for Low-Light Image Enhancement. – Optics Express, Vol. 31, 2023, No 26, 42887.
Search in Google Scholar Back to article
Zhang, W., H. Zhang, X. Liu, X. Guo, X. Wang, S. Li. Unsupervised Low-Light Image Enhancement Based on Explicit Denoising and Knowledge Distillation. – Computers, Materials and Continua, Vol. 82, 2025, No 2, pp. 2537-2554.
Search in Google Scholar Back to article
Yan, Q., Y. Feng, C. Zhang, P. Wang, P. Wu, W. Dong, J. Sun, Y. Zhang. You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement. – arXiv Preprint arXiv 2402.05809, Vol. 2024, pp. 1-11.
Search in Google Scholar Back to article
Cho, S.-J., S.-W. Ji, J.-P. Hong, S.-W. Jung, S.-J. Ko. Rethinking Coarse-to-Fine Approach in Single Image Deblurring. – In: Proc. of IEEE/CVF International Conference on Computer Vision (ICCV’21), IEEE, 2021, pp. 4621-4630.
Search in Google Scholar Back to article
Mittal, A., R. Soundararajan, A. C. Bovik. Making a “Completely Blind” Image Quality Analyzer. – IEEE Signal Processing Letters, Vol. 20, 2013, No 3, pp. 209-212.
Search in Google Scholar Back to article

ALEX: Automated Low-Light Enhancement eXpert for Intelligent Security Systems Using Vision Transformer

Abstract

Paradigm

My account