| Peer-Reviewed

Low Light Image Enhancement for Dark Images

Received: 10 May 2020     Accepted: 25 May 2020     Published: 7 September 2020
Views:       Downloads:
Abstract

Image plays an important role in this present technological world and leads to progress in multimedia communication, various research fields related to image processing, etc. Low-light image enhancement specifically addresses images captured in low-light conditions such as nighttime, where the common goal is to brighten and improve the contrast of the image for better visual quality and show details that are hidden in darkness. Research fields that may assist us in lowlight environments, such as object detection, has glossed over this aspect even though breakthroughs-after breakthroughs had been achieved in recent years, most noticeably from the lack of low-light data (less than 2% of the total images) in successful public benchmark datasets such as PASCAL VOC, ImageNet, and Microsoft COCO. To improve image quality, these low-light images are needed to be enhanced. For this purpose, an exclusively dark dataset comprising of images captured in visible light only is proposed. Further, dehazing technique is used for haze removal, histogram equalization (HE) technique is used for contrast enhancement and denoising technique is used for noise removal. Experimental results demonstrate that the proposed method achieves a good performance in low light image enhancement and outperforms state-of-the-art ones in terms of contrast enhancement and noise reduction.

Published in International Journal of Data Science and Analysis (Volume 6, Issue 4)
DOI 10.11648/j.ijdsa.20200604.11
Page(s) 99-104
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Dataset, Dehazing, Denoising, Enhancement, Histogram Equalization, Low-light

References
[1] Chen, C., Chen, Q., Xu, J., Koltun, V., 2018. Learning to see in the dark. In: Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference.
[2] Jung, C., Yang, Q., Sun, T., Fu, Q., Song, H., 2017. Low light image enhancement with dual-tree complex wavelet transform. J. Vis. Commun. Image Represent. 42, 28–36.
[3] Li, M., Liu, J., Yang, W., Sun, X., Guo, Z., 2018. Structure-revealing low-light image enhancement via robust retinex model. IEEE Trans. Image Process. 27, 2828–2841.
[4] Y. P. Loh, C. S. Chan, Getting to know low-light images with the exclusively dark dataset, Comput. Vis. Image Understanding 178 (2018) 30–42.
[5] Everingham, M., Eslami, S. A., Van Gool, L., Williams, C. K., Winn, J., Zisserman, A., 2015. The pascal visual object classes challenge: A retrospective. Int. J. Comput. Vis. 111, 98–136.
[6] Guo, X., Li, Y., Ling, H., 2017. Lime: Low-light image enhancement via illumination map estimation. IEEE Trans. Image Process. 26, 982–993.
[7] Lim, J., Kim, J. H., Sim, J. Y., Kim, C. S., 2015. Robust contrast enhancement of noisy lowlight images: Denoising-enhancement-completion. In: Image Processing (ICIP), 2015 IEEE International Conference on. IEEE, pp. 4131–4135.
[8] Li, L., Wang, R., Wang, W., Gao, W., 2015. A low-light image enhancement method for both denoising and contrast enlarging. In: Image Processing (ICIP), 2015 IEEE International Conference on. IEEE, pp. 3730–3734.
[9] K. He, J. Sun and X. Tang: Single Image Haze Removal Using Dark Channel Prior, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 12, 2011, pp. 2341 − 2353.
[10] Q. Zhu, J. Mai and L. Shao, “A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior,” IEEE Transactions on Image Processing, Vol. 24, No. 11, 2015, pp. 3522 − 3533.
[11] S. Puzović, R. Petrović, M. Pavlović and S. Stanković, "Enhancement Algorithms for Low-Light and Low-Contrast Images," 2020 19th International Symposium INFOTEH-JAHORINA (INFOTEH), East Sarajevo, Bosnia and Herzegovina, 2020, pp. 1-6, doi: 10.1109/INFOTEH48170.2020.9066316.
[12] Shen, L., Yue, Z., Feng, F., Chen, Q., Liu, S., Ma, J., 2017. Msr-net: Low-light image enhancement using deep convolutional network. arXiv preprint arXiv: 1711.02488.
[13] Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y., 2010. Locality-constrained linear coding for image classification. In: Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on. IEEE, pp. 3360–3367.
[14] Leo, M., Medioni, G., Trivedi, M., Kanade, T., Farinella, G. M., 2017. Computer vision for assistive technologies. Comput. Vision Image Understanding 154, 1–15.
[15] Mahendran, A., Vedaldi, A., 2015. Understanding deep image representations by inverting them. In: Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on. pp. 5188–5196.
[16] Remez, T., Litany, O., Giryes, R., Bronstein, A. M., 2017. Deep convolutional denoising of low-light images. arXiv preprint arXiv: 1701.01687.
Cite This Article
  • APA Style

    Akshay Patil, Tejas Chaudhari, Ketan Deo, Kalpesh Sonawane, Rupali Bora. (2020). Low Light Image Enhancement for Dark Images. International Journal of Data Science and Analysis, 6(4), 99-104. https://doi.org/10.11648/j.ijdsa.20200604.11

    Copy | Download

    ACS Style

    Akshay Patil; Tejas Chaudhari; Ketan Deo; Kalpesh Sonawane; Rupali Bora. Low Light Image Enhancement for Dark Images. Int. J. Data Sci. Anal. 2020, 6(4), 99-104. doi: 10.11648/j.ijdsa.20200604.11

    Copy | Download

    AMA Style

    Akshay Patil, Tejas Chaudhari, Ketan Deo, Kalpesh Sonawane, Rupali Bora. Low Light Image Enhancement for Dark Images. Int J Data Sci Anal. 2020;6(4):99-104. doi: 10.11648/j.ijdsa.20200604.11

    Copy | Download

  • @article{10.11648/j.ijdsa.20200604.11,
      author = {Akshay Patil and Tejas Chaudhari and Ketan Deo and Kalpesh Sonawane and Rupali Bora},
      title = {Low Light Image Enhancement for Dark Images},
      journal = {International Journal of Data Science and Analysis},
      volume = {6},
      number = {4},
      pages = {99-104},
      doi = {10.11648/j.ijdsa.20200604.11},
      url = {https://doi.org/10.11648/j.ijdsa.20200604.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20200604.11},
      abstract = {Image plays an important role in this present technological world and leads to progress in multimedia communication, various research fields related to image processing, etc. Low-light image enhancement specifically addresses images captured in low-light conditions such as nighttime, where the common goal is to brighten and improve the contrast of the image for better visual quality and show details that are hidden in darkness. Research fields that may assist us in lowlight environments, such as object detection, has glossed over this aspect even though breakthroughs-after breakthroughs had been achieved in recent years, most noticeably from the lack of low-light data (less than 2% of the total images) in successful public benchmark datasets such as PASCAL VOC, ImageNet, and Microsoft COCO. To improve image quality, these low-light images are needed to be enhanced. For this purpose, an exclusively dark dataset comprising of images captured in visible light only is proposed. Further, dehazing technique is used for haze removal, histogram equalization (HE) technique is used for contrast enhancement and denoising technique is used for noise removal. Experimental results demonstrate that the proposed method achieves a good performance in low light image enhancement and outperforms state-of-the-art ones in terms of contrast enhancement and noise reduction.},
     year = {2020}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Low Light Image Enhancement for Dark Images
    AU  - Akshay Patil
    AU  - Tejas Chaudhari
    AU  - Ketan Deo
    AU  - Kalpesh Sonawane
    AU  - Rupali Bora
    Y1  - 2020/09/07
    PY  - 2020
    N1  - https://doi.org/10.11648/j.ijdsa.20200604.11
    DO  - 10.11648/j.ijdsa.20200604.11
    T2  - International Journal of Data Science and Analysis
    JF  - International Journal of Data Science and Analysis
    JO  - International Journal of Data Science and Analysis
    SP  - 99
    EP  - 104
    PB  - Science Publishing Group
    SN  - 2575-1891
    UR  - https://doi.org/10.11648/j.ijdsa.20200604.11
    AB  - Image plays an important role in this present technological world and leads to progress in multimedia communication, various research fields related to image processing, etc. Low-light image enhancement specifically addresses images captured in low-light conditions such as nighttime, where the common goal is to brighten and improve the contrast of the image for better visual quality and show details that are hidden in darkness. Research fields that may assist us in lowlight environments, such as object detection, has glossed over this aspect even though breakthroughs-after breakthroughs had been achieved in recent years, most noticeably from the lack of low-light data (less than 2% of the total images) in successful public benchmark datasets such as PASCAL VOC, ImageNet, and Microsoft COCO. To improve image quality, these low-light images are needed to be enhanced. For this purpose, an exclusively dark dataset comprising of images captured in visible light only is proposed. Further, dehazing technique is used for haze removal, histogram equalization (HE) technique is used for contrast enhancement and denoising technique is used for noise removal. Experimental results demonstrate that the proposed method achieves a good performance in low light image enhancement and outperforms state-of-the-art ones in terms of contrast enhancement and noise reduction.
    VL  - 6
    IS  - 4
    ER  - 

    Copy | Download

Author Information
  • Information Technology, K. K. Wagh Institute of Engineering Education and Research, Nashik, India

  • Information Technology, K. K. Wagh Institute of Engineering Education and Research, Nashik, India

  • Information Technology, K. K. Wagh Institute of Engineering Education and Research, Nashik, India

  • Information Technology, K. K. Wagh Institute of Engineering Education and Research, Nashik, India

  • Information Technology, K. K. Wagh Institute of Engineering Education and Research, Nashik, India

  • Sections