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A single backlit image enhancement method by image fusion with a weight map for improvement of dark area’s visibility

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Abstract

Images captured in backlit conditions (i.e., backlit images) often have a vast difference in lightness between bright and dark areas. In such a dark area in an image, the visibility becomes extremely low, making it indistinct to recognize the subject. Sufficient image quality cannot be obtained by simply applying a general image enhancement method to such a backlit image. Many methods specializing in improving the image quality of backlit images have been proposed to cope with this problem. Although these methods can effectively improve dark areas’ visibility compared to general image enhancement methods, the enhancement process causes artifacts in bright areas. In this paper, we propose a single backlit image enhancement method that effectively improves only the visibility of dark areas while suppressing over-enhancement and artifacts. In the proposed method, the lightness of the output image is calculated by the weighted sum of the input lightness image and the enhanced lightness image based on a weight map. The enhanced lightness image is calculated by alpha-blending two lightness-converted images obtained by gamma conversion and histogram equalization of the input lightness image. The weight map is calculated based on edge-preserving smoothing with a guided filter of a binarized input lightness image obtained using Otsu’s method. The experiment shows the proposed method’s effectiveness by quantitatively and qualitatively comparing conventional image enhancement methods and the proposed method using various backlit images.

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References

  1. Lukac, R.: Single-sensor imaging: methods and applications for digital cameras, CRC Press Taylor & Francis Group, pp. 336–337, (2009)

  2. Gonzalez, R.C., Woods, R.E.: Digital image processing, 2nd edn. Prentice Hall, New Jersey (2002)

    Google Scholar 

  3. Acharya, T., Ray, A.K.: Image processing - principles and applications. Wiley-Interscience, USA (2005)

    Book  Google Scholar 

  4. Zuiderveld, K.: “Contrast limited adaptive histogram equalization,” In: Graphic gems IV, pp. 474–485. Academic Press, Cambridge (1994)

  5. Rahman, Z., Woodell, G.A.: A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image Process. 6, 965–976 (1997)

    Article  ADS  Google Scholar 

  6. Hao, S., Han, X., Guo, Y., Xu, X., Wang, M.: Low-light image enhancement with semi-decoupled decomposition. IEEE Trans. Multimedia 22(12), 3025–3038 (2020)

    Article  Google Scholar 

  7. Wang, Q., Fu, X., Zhang, X., Ding, X.: “A fusion-based method for single backlit image enhancement,” In Proc. IEEE Int. Conf. Image Process. (ICIP), pp. 4077–4081, (2016)

  8. Buades, A., Lisani, J.L., Petro, A.B., Sbert, C.: Backlit images enhancement using global tone mappings and image fusion. IET Image Process. 14(2), 211–219 (2020)

    Article  Google Scholar 

  9. Burt, P.J., Adelson, E.H.: The laplacian pyramid as a compact image code. IEEE Trans. Commun. 31(4), 532–540 (1983)

    Article  Google Scholar 

  10. Deng, G.: A generalized unsharp masking algorithm. IEEE Trans. Image Process. 20(5), 1249–1261 (2011)

    Article  ADS  MathSciNet  Google Scholar 

  11. Mertens, T., Kautz, J., Reeth, F.V.: “Exposure fusion,” In Proc. of 15th Pacific Conf. on Computer Graphics and Applications, Hawaii, USA, pp. 382-390, (2007)

  12. Li, Z., Cheng, K., Wu, X.: “Soft binary segmentation-based backlit image enhancement,” In Proc. IEEE Int. Workshop on Multimedia Signal Processing, pp. 1–5, (2015)

  13. Li, Z., Wu, X.: Learning-based restoration of backlit images. IEEE Trans. Image Process. 27(2), 976–986 (2018)

    Article  ADS  MathSciNet  Google Scholar 

  14. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  Google Scholar 

  15. Kaiming, H., Jian, S., Xiaoou, T.: Guided Image Filtering,. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2012)

    Google Scholar 

  16. Shin, J., Oh, H., Kim, K., Kang, K.: Automatic image enhancement for under-exposed, over-exposed, or backlit images. Electronic Imaging 2019(14), 088-1-088–6 (2019)

    Article  Google Scholar 

  17. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vision 57(2), 137–154 (2004)

    Article  Google Scholar 

  18. Wang, S., Zheng, J., Hu, H., Li, B.: Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Trans. Image Process. 22(9), 3538–3548 (2013)

    Article  ADS  Google Scholar 

  19. Ying, Z., Li, G., Ren, Y., Wang, R., Wang, W.: “A new low light image enhancement algorithm using camera response model,” In Proc. IEEE Int. Conf. on Computer Vision Workshops (ICCVW), pp. 3015–3022, (2017)

  20. Guo, X., Li, Y., Ling, H.: Lime: low-light image enhancement via illumination map estimation. IEEE Trans. Image Process. 26(2), 982–993 (2017)

    Article  ADS  MathSciNet  Google Scholar 

  21. Jobson, D.J., Rahman, Z., Woodell, G.A.: The statistics of visual representation. Proc. SPIE 4736, 25–35 (2002)

    Article  ADS  Google Scholar 

  22. Gu, K., Tao, D., Qiao, J., Lin, W.: Learning a no-reference quality assessment model of enhanced images with big data. IEEE Trans. Neural Networks Learn. 29(4), 1301–1313 (2018)

    Article  Google Scholar 

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Authors and Affiliations

  1. Graduate School of Science and Technology for Innovation, Yamaguchi University, 1677-1 Yoshida, Yamaguchi-shi, 753-8512, Japan

    Masato Akai & Noriaki Suetake

  2. Faculty of Science, Fukuoka University, 8-19-1 Nanakuma, Jonan-ku, Fukuoka-shi, 814-0180, Japan

    Yoshiaki Ueda

  3. Graduate School of Humanity-Oriented Science and Engineering, Kindai University, 11-6 Kayanomori, Iizuka-shi, 820-8555, Japan

    Takanori Koga

Authors
  1. Masato Akai
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  2. Yoshiaki Ueda
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  3. Takanori Koga
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  4. Noriaki Suetake
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Correspondence to Noriaki Suetake.

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Akai, M., Ueda, Y., Koga, T. et al. A single backlit image enhancement method by image fusion with a weight map for improvement of dark area’s visibility. Opt Rev 29, 69–79 (2022). https://doi.org/10.1007/s10043-022-00725-4

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  • DOI: https://doi.org/10.1007/s10043-022-00725-4

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