TEJAS Journal of Technologies and Humanitarian Science

Shivlok Singh
Principal Technical Officer, NIELIT Delhi Centre, Delhi, India.

Author

M Ravi Kumar Reddy
WBL-Intern, NIELIT Delhi Centre, Delhi, Indi

Author

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Abstract:

Low-light image enhancement is a key prerequisite for diverse applications in the field of image processing and computer vision. Various approaches for this task have been introduced over last few decades, and the current state of the art methods have shown remarkable advances based on deep neural networks. However, there are still technical issues to be resolved, e.g., dependency on subjective re-touching results and inconsistency with subjective evaluations. The goal of this work is to provide a comprehensive overview and a practical guide for experts as well as beginners. This paper covers a comparison of two models 1. Zero DCE 2. Enlighten GANs, representative methodologies, and the performance analysis on benchmark datasets. To pave the way of the development direction for low-light image enhancement.

References

[1]. OrCam Technologies. (2021). OrCam MyEye: Assistive Vision Solutions. Whitepaper.
[2]. Wang, T., Fu, X., Wang, J., Hu, Y., Huang, Q., & Ding, X. (2020). Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1780–1789.
[3]. Zhang, Q., et al. (2021). Adaptive Assistive Technology for Visually Impaired Navigation Using Tactile Feedback. IEEE Sensors Journal.
[4]. Kim, J., & Lee, H. (2021). Real-Time Navigation System for Low-Vision Users. IEEE Transactions on Consumer Electronics.
[5]. Su, S., & Zhao, Q. (2020). GAN-Based Real-Time Object Detection in Low-Light Environments for Navigation Assistance. IEEE Access.
[6]. Guo, Y., Zhang, L., & Li, Y. (2020). Zero-reference deep curve estimation for low-light image enhancement. IEEE Transactions on Image Processing, 29, 2821-2832.
[7]. Xu, K., Yang, X., Yin, B., & Lau, R. W. H. (2020). Learning to Restore Low-Light Images via Decomposition-and-Enhancement. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2281-2290.
[8]. Wang, X., et al. (2019). Enhanced Image Enhancement Using Deep Curve Estimation. CVPR.
[9]. Ren, S., He, K., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems (NeurIPS), 28, 91-99.