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A Novel Fusion Method for Low Brightness Enhancement Derivatives

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Abstract

In this paper, a straightforward and effective fusion method is designed for low brightness enhancement derivatives, which are generated through using brightness enhancement technique for a single low-brightness image. First, illumination estimation techniques and the principle of retinal imaging and cerebral cortex adjustment are combined to acquire the exposure ratio map. Then, a novel Chi-squared conversion function model and an accurate exposure ratio map are employed to obtain two derivatives with different characteristics: one is natural but not very detailed; the other is excessively bright but with prominent details. Finally, the improved weight matrix design and a novel derivatives fusion method are utilized to fuse the improved features of the derivatives. Experiments on a diverse set of images demonstrate that the proposed algorithm can not only reveal the efficiency of the brightness and detail enhancement, but also can show its superiority over several state-of-the-art processes in terms of overall visual information enhancement.

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References

  1. A. Ahmad et al., Noise resistant fusion for multi-exposure sensors. IEEE Sens. J. 16(13), 5123–5124 (2016)

    Article  Google Scholar 

  2. B.P. Ana, C. Sbert, J.M. Morel, Multiscale retinex. Image Process. Line 4, 71–88 (2014)

    Article  Google Scholar 

  3. D.H. Brainard, B.A. Wandell, Analysis of the retinex theory of color vision. J. Opt. Soc. Am. A Opt. Image Sci. Vis. 3(10), 1651–1661 (1986)

    Article  Google Scholar 

  4. Y. Chen, Y. Wang, M. Kao, et al., Deep photo enhancer: unpaired learning for image enhancement from photographs with GANs, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT (2018), pp. 6306–6314

  5. A.V. Deshmukh, T.G. Patil, S.S. Patankar, et al., Features based classification of hard exudates in retinal images, in 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (2015), pp. 1652–1655

  6. X. Dong, G. Wang, Y. Pang, et al., Fast efficient algorithm for enhancement of low lighting video, in 2011 IEEE International Conference on Multimedia and Expo (2011), pp. 1–6

  7. X. Fu, D. Zeng, Y. Huang et al., A fusion-based enhancing method for weakly illuminated images. Sig. Process. 129, 82–96 (2016)

    Article  Google Scholar 

  8. X. Fu, D. Zeng, Y. Huang, et al., A weighted variational model for simultaneous reflectance and illumination estimation, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016), pp. 2782–2790

  9. P. Godara et al., Adaptive optics retinal imaging: emerging clinical applications. Optom. Vis. Sci. 87(12), 930–941 (2010)

    Article  Google Scholar 

  10. B. Gu et al., Gradient field multi-exposure images fusion for high dynamic range image visualization. J. Vis. Commun. Image Represent. 23(4), 604–610 (2012)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  12. N. Hayashi et al., Accuracy of the backward-difference solution of the Landau–Lifshitz–Gilbert equation. Jpn. J. Appl. Phys. 42(3), 1250–1257 (2003)

    Article  Google Scholar 

  13. T. Hoang, B. Pan, D. Nguyen et al., Generic gamma correction for accuracy enhancement in fringe-projection profilometry. Opt. Lett. 35(12), 1992 (2010)

    Article  Google Scholar 

  14. J. Hu, S. Li, The multiscale directional bilateral filter and its application to multisensor image fusion. Inf. Fus. 13(3), 196–206 (2012)

    Article  Google Scholar 

  15. D.J. Jobson, Z. Rahman, G.A. Woodell, A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image Process. 6(7), 965–976 (1997)

    Article  Google Scholar 

  16. H.J. Kwon, S.H. Lee, G.Y. Lee et al., Luminance adaptation transform based on brightness functions for LDR image reproduction. Digit. Signal Proc. 30, 74–85 (2014)

    Article  Google Scholar 

  17. E.H. Land, The retinex theory of color vision. Sci. Am. 237(6), 108 (1977)

    Article  Google Scholar 

  18. S. Lee, An efficient content-based image enhancement in the compressed domain using retinex theory. IEEE Trans. Circuits Syst. Video Technol. 17(2), 199–213 (2007)

    Article  Google Scholar 

  19. S. Li, X. Kang, Fast multi-exposure image fusion with median filter and recursive filter. IEEE Trans. Consum. Electron. 58(2), 626–632 (2012)

    Article  Google Scholar 

  20. Q. Liu, H. Leung, Variable augmented neural network for decolorization and multi-exposure fusion. Inf. Fus. 46, 114–127 (2018)

    Article  Google Scholar 

  21. K. Ma et al., Perceptual quality assessment for multi-exposure image fusion. IEEE Trans. Image Process. 24(11), 3345–3356 (2015)

    Article  MathSciNet  Google Scholar 

  22. T. Mertens, J. Kautz, F.V. Reeth, Exposure fusion, in Conference on Computer Graphics and Applications (2007)

  23. N. Moleele, S. Ringrose, W. Arnberg et al., Assessment of vegetation indexes useful for browse (forage) prediction in semi-arid rangelands. Int. J. Remote Sens. 22(5), 741–756 (2001)

    Article  Google Scholar 

  24. T.H. Oh et al., Robust high dynamic range imaging by rank minimization. IEEE Trans. Pattern Anal. Mach. Intell. 37(6), 1219–1232 (2015)

    Article  Google Scholar 

  25. K. Peng et al., Perceptual multi-exposure image fusion with overall image quality index and local saturation. Multimed. Syst. 23(2), 239–250 (2017)

    Article  Google Scholar 

  26. V.S. Petrović, C.S. Xydeas, Sensor noise effects on signal-level image fusion performance. Inf. Fus. 4(3), 167–183 (2003)

    Article  Google Scholar 

  27. R. Ramanath, W.E. Snyder, Demosaicking as a bilateral filtering process. Proc. Spie 4667, 236–244 (2002)

    Article  Google Scholar 

  28. Y. Ren, Z. Ying, T.H. Li et al., LECARM: low-light image enhancement using camera response model. IEEE Trans. Circuits Syst. Video Technol. 99, 1 (2018)

    Google Scholar 

  29. M. Shakeri et al., Image contrast enhancement using fuzzy clustering with adaptive cluster parameter and sub-histogram equalization. Digit. Signal Proc. 62, 224–237 (2017)

    Article  Google Scholar 

  30. L. Shao et al., Spatio-temporal Laplacian pyramid coding for action recognition. IEEE Trans. Cybern. 44(6), 817 (2014)

    Article  Google Scholar 

  31. H.R. Sheikh, A.C. Bovik, Image information and visual quality. IEEE Trans. Image Process. 15(2), 430–444 (2006)

    Article  Google Scholar 

  32. R. Shen, I. Cheng, A. Basu, QoE-based multi-exposure fusion in hierarchical multivariate Gaussian CRF. IEEE Trans. Image Process. 22(6), 2469–2478 (2013)

    Article  MathSciNet  Google Scholar 

  33. M.O. Si-Te, T.Q. Liu, L.I. Bi-Xiong, Research of automatic white balance algorithm based on HSL color space. Sichuan Daxue Xuebao 45, 95–99 (2013)

    Google Scholar 

  34. A.V. Vanmali, V.M. Gadre, Visible and NIR image fusion using weight-map-guided Laplacian–Gaussian pyramid for improving scene visibility. Sādhanā 42(7), 1063–1082 (2017)

    Article  Google Scholar 

  35. L. Wang et al., Variational Bayesian method for retinex. IEEE Trans. Image Process. 23(8), 3381–3396 (2014)

    Article  MathSciNet  Google Scholar 

  36. S. Wang, J. Zheng, H.M. Hu et al., Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Trans. Image Process. 22(9), 3538–3548 (2013)

    Article  Google Scholar 

  37. C. Wei, W. Wang, W. Yang, et al., Deep retinex decomposition for low-light enhancement, in Proceedings of the British Machine Vision Conference (BMVC) (2018), p. 1

  38. S. Wu et al., An improved image enhancement approach based on retinex theory, in 2013 International Conference on Information Technology and Applications, ITA 2013, November 16, 2013–November 17, 2013, Chengdu, China (IEEE Computer Society, 2013) pp. 67–71

  39. L. Xu et al., Structure extraction from texture via relative total variation. ACM Trans. Graph. 31(6), 1 (2012)

    Google Scholar 

  40. Z. Ying, G. Li, Y. Ren, et al., A new low-light image enhancement algorithm using camera response model, in 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) (2017), pp. 3015–3022

  41. S.H. Yun et al., Image enhancement using a fusion framework of histogram equalization and Laplacian pyramid. IEEE Trans. Consum. Electron. 56(4), 2763–2771 (2010)

    Article  Google Scholar 

  42. W. Zhang, W.K. Cham, Gradient-directed multiexposure composition. IEEE Trans. Image Process. 21(4), 2318–2323 (2012)

    Article  MathSciNet  Google Scholar 

  43. W. Zhang, W.K. Cham, Reference-guided exposure fusion in dynamic scenes. J. Vis. Commun. Image Represent. 23(3), 467–475 (2012)

    Article  Google Scholar 

Download references

Acknowledgements

This project was supported the National Natural Science Foundation of China (81741008), the Natural Science Foundation of Fujian Province (2019J01272), the Program for Changjiang Scholars and Innovative Research Team in University (IRT_15R10), the Special Funds of the Central Government Guiding Local Science and Technology Development (2017L3009) and the Scientific Research Innovation Team Construction Program of Fujian Normal University (IRTL1702).

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Authors and Affiliations

  1. Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, 350007, People’s Republic of China

    Chao Wei, Hongxin Lin, Lijuan Tang, Weiping Liu, Mengzhen Jiang, Junfeng Wang & Guannan Chen

  2. Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou, 350117, Fujian, People’s Republic of China

    Chao Wei, Lijuan Tang, Weiping Liu, Mengzhen Jiang, Junfeng Wang & Guannan Chen

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  1. Chao Wei
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  2. Hongxin Lin
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  3. Lijuan Tang
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  5. Mengzhen Jiang
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  7. Guannan Chen
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Correspondence to Guannan Chen.

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Wei, C., Lin, H., Tang, L. et al. A Novel Fusion Method for Low Brightness Enhancement Derivatives. Circuits Syst Signal Process 40, 335–352 (2021). https://doi.org/10.1007/s00034-020-01474-y

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