DEEP LEARNING MODELS FOR ENHANCING LOW-LIGHT PHOTOGRAPHY

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

The Low-light photography often suffers from issues such as noise, low contrast, and color distortion, which degrade image quality and limit its applicability in various fields such as night-time photography, security surveillance, and autonomous driving. Traditional image enhancement techniques struggle to adequately address these challenges due to the complex nature of noise and illumination variations in low-light conditions. In recent years, deep learning models have emerged as powerful tools for enhancing low-light images, offering significant improvements over conventional methods. This paper reviews the state-of-the-art deep learning approaches specifically designed for low-light photography enhancement. We explore various architectures, including Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Transformer-based models, that have been tailored to restore details, improve brightness, and reduce noise in low-light images. Key techniques discussed include supervised learning approaches that leverage large-scale datasets with paired low-light and normal-light images, as well as unsupervised and semi-supervised methods that overcome the limitations of labeled data availability. Additionally, we examine the role of domain-specific loss functions, such as perceptual and adversarial losses, in achieving photorealistic enhancements. The paper also highlights the challenges and limitations of current models, such as computational complexity and generalization to diverse lighting conditions. We conclude by suggesting future research directions, including the integration of deep learning with traditional image processing techniques, real-time enhancement capabilities, and the development of models that are robust to varying degrees of illumination and noise levels. This comprehensive review aims to provide insights into the potential of deep learning models for advancing the field of low-light photography, ultimately contributing to broader applications in both consumer and professional imaging.

References

[1] Wang, Zhou, et al. Deep Learning for Image Enhancement and Restoration. Springer, 2021.

[2] Bovik, Alan C. The Essential Guide to Image Processing. Academic Press, 2020.

[3] Kong, Xiaofeng, and Jie Liu. Computational Photography: Image Processing for Low-Light and HDR Applications. CRC Press, 2019.

[4] Chen, Chen, et al. "Learning to See in the Dark." IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 3291-3300, doi:10.1109/CVPR.2018.00346.

[5] Guo, Xintao, et al. "Low-Light Image Enhancement via Deep Retinex-Net." IEEE Transactions on Image Processing, vol. 29, 2020, pp. 5956–5966, doi:10.1109/TIP.2020.2995627.

[6] Lore, Kin Gwn, et al. "LLNet: A Deep Autoencoder Approach to Natural Low-Light Image Enhancement." Pattern Recognition, vol. 61, 2017, pp. 650–662, doi:10.1016/j.patcog.2016.06.008.

[7] Zhang, Kai, et al. "Deep Image Denoising and Enhancement for Low-Light Vision." International Journal of Computer Vision, vol. 128, no. 2, 2020, pp. 501–525, doi: 10.1007/s11263-019-01284-5.

[8] Jiang, Yifan, et al. "EnlightenGAN: Deep Light Enhancement without Paired Supervision." Proceedings of the
British Machine Vision Conference (BMVC), 2019, pp. 148-156.

[9] Zamir, Syed Waqas, et al. "Learning Enriched Features for Real Low-Light Image Enhancement." Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 1255–1264, doi:10.1109/ICCV.2021.00138.

[10] Advances in Low-Light Image Enhancement Using AI. NVIDIA Research, 2022.

[11] Rajkumar, V., and V. Maniraj. "HYBRID TRAFFIC ALLOCATION USING APPLICATION-AWARE ALLOCATION OF RESOURCES IN CELLULAR NETWORKS." Shodhsamhita (ISSN: 2277-7067) 12.8 (2021).

[12] Ambika, G., and P. Srivaramangai. "REVIEW ON SECURITY IN THE INTERNET OF THINGS." International Journal of Advanced Research in Computer Science 9.1 (2018).

[13] Rosy, C. Premila, and R. Ponnusamy. "Evaluating and forecasting room demand in tourist spot using Holt-Winters method." International Journal of Computer Applications 975 (2017): 8887.

[14] D.Ragupathi, N.Jayaveeran, “The Design & Implementation of Transportation Procedure using Migration Techiques,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.273-278, 2017.

[15] Rajkumar, V., and V. Maniraj. "RL-ROUTING: A DEEP REINFORCEMENT LEARNING SDN ROUTING ALGORITHM." JOURNAL OF EDUCATION: RABINDRABHARATI UNIVERSITY (ISSN: 0972-7175) 24.12 (2021).

[16] Ambika, G., and P. Srivaramangai. "A study on data security in Internet of Things." Int. J. Comput. Trends Technol. 5.2 (2017): 464-469.

[17] C.Senthil Selvi, Dr. N. Vetrivelan, “Medical Search Engine Based On Enhanced Best First Search International Journal Of Research And Analytical Reviews (IJRAR.ORG) 2019, Volume 6, Issue 2, Page No: 248-250.

[18] Rajkumar, V., and V. Maniraj. "Software-Defined Networking's Study with Impact on Network Security." Design Engineering (ISSN: 0011-9342) 8 (2021).

[19] Ambika, G., and D. P. Srivaramangai. "A study on security in the Internet of Things." Int. J. Sci. Res. Comput. Sci. Eng. Inform. Technol 5.2 (2017): 12-21.

[20] K.U. Malar, D. Ragupathi, G.M. Prabhu, “The Hadoop Dispersed File system: Balancing Movability and Performance”, International Journal of Computer Sciences and Engineering, Vol.2, Issue.9, pp.166-177, 2014.

[21] D. Ragupathi, S.Sivaranjani, “Performance Enhanced Live Migration of Virtual Machines in the Cloud,” International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.94-99, 2015.

[22] Rosy, C. P. R. O. M., and R. Ponnusamy. "A Study on Hotel Reservation Trends of Mobile App via Smartphone." IOSR Journal of Computer Engineering (IOSR-JCE) 19.4 (2017): 01-08.

[23] Rajkumar, V., and V. Maniraj. "HCCLBA: Hop-By-Hop Consumption Conscious Load Balancing Architecture Using Programmable Data Planes." Webology (ISSN: 1735-188X) 18.2 (2021).

[24] Ambika, G., and P. Srivaramangai. "Encrypted Query Data Processing in Internet Of Things (IoTs): CryptDB and Trusted DB." (2018).

[25] Rajkumar, V., and V. Maniraj. "Dependency Aware Caching (Dac) For Software Defined Networks." Webology (ISSN: 1735-188X) 18.5 (2021).

[26] C.Senthil Selvi, Dr. N. Vetrivelan, “ An Efficient Information Retrieval In Mesh (Medical Subject Headings) Using Fuzzy”, Journal of Theoretical and Applied Information Technology 2019. ISSN: 1992-8645, Vol.97. No 9, Page No: 2561-2571.

[27] Rosy, C. Premila, and R. Ponnusamy. "Intelligent System to Support Judgmental Business Forecasting: The Case of Unconstraint Hotel RoomDemand in Hotel Advisory System." International Journal of Science and Research (IJSR) 4.1 (2015).

[28] C.Senthil Selvi, Dr. N. Vetrivelan, “Medical Search Engine Based On Enhanced Best First Search International Journal Of Research And Analytical Reviews (IJRAR.ORG) 2019, Volume 6, Issue 2, Page No: 248-250.

[29] D. Ragupathi and N. Jayaveeran, "Significant role of migration in virtual environment," 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS), Pudukkottai, India, 2016, pp. 1-6, doi: 10.1109/ICETETS.2016.7603122.

[30] M. Dhivya, D. Ragupathi, V.R. Kumar, “Hadoop Mapreduce Outline in Big Figures Analytics,” International Journal of Computer Sciences and Engineering, Vol.2, Issue.9, pp.100-104, 2014.

Keywords

Low-Light Image Enhancement, Deep Learning for Night Photography, Noise Reduction in Dark Images, Neural Networks for Image Denoising, Contrast and Brightness Optimization.