Multimedia Tools and Applications

, Volume 78, Issue 14, pp 20263–20283 | Cite as

Generation of high dynamic range illumination from a single image for the enhancement of undesirably illuminated images

  • Jae Sung Park
  • Jae Woong Soh
  • Nam Ik ChoEmail author


This paper presents an algorithm that enhances undesirably illuminated images by generating and fusing multi-level illuminations from a single image. The input image is first decomposed into illumination and reflectance components by using an edge-preserving smoothing filter. Then the reflectance component is scaled up to improve the image details in bright areas. The illumination component is scaled up and down to generate several illumination images that correspond to certain camera exposure values different from the original. The virtual multi-exposure illuminations are blended into an enhanced illumination, where we also propose a method to generate appropriate weight maps for the tone fusion. Finally, an enhanced image is obtained by multiplying the equalized illumination and enhanced reflectance. Experiments show that the proposed algorithm produces visually pleasing output and also yields comparable objective results to the conventional enhancement methods, while requiring modest computational loads.


High dynamic range imaging Single-image high dynamic range imaging Image enhancement Illumination adjustment Multi-exposure fusion 



This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2018-2016-0-00288) supervised by the IITP(Institute for Information & communications Technology Promotion).


  1. 1.
    Ahn B, Cho NI (2017) Block-matching convolutional neural network for image denoising. arXiv:170400524
  2. 2.
    Akyüz AO, Fleming R, Riecke BE, Reinhard E, Bülthoff HH (2007) Do hdr displays support ldr content?: a psychophysical evaluation. ACM Trans Graph (TOG) 26(3):38Google Scholar
  3. 3.
    An J, Lee SH, Kuk JG, Cho NI (2011) A multi-exposure image fusion algorithm without ghost effect. In: 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 1565–1568Google Scholar
  4. 4.
    An J, Ha SJ, Cho NI (2012) Reduction of ghost effect in exposure fusion by detecting the ghost pixels in saturated and non-saturated regions. In: 2012 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 1101–1104Google Scholar
  5. 5.
    An J, Ha SJ, Cho NI (2014) Probabilistic motion pixel detection for the reduction of ghost artifacts in high dynamic range images from multiple exposures. EURASIP 2014:1–15Google Scholar
  6. 6.
    Arici T, Dikbas S, Altunbasak Y (2009) A histogram modification framework and its application for image contrast enhancement. IEEE Trans Image Process 18(9):1921–1935MathSciNetzbMATHGoogle Scholar
  7. 7.
    Banterle F, Ledda P, Debattista K, Chalmers A (2006) Inverse tone mapping. In: Proceedings of the 4th international conference on computer graphics and interactive techniques in Australasia and Southeast Asia, ACM, pp 349–356Google Scholar
  8. 8.
    Banterle F, Ledda P, Debattista K, Chalmers A (2008) Expanding low dynamic range videos for high dynamic range applications. In: Proceedings of the 24th spring conference on computer graphics, ACM, pp 33–41Google Scholar
  9. 9.
    Banterle F, Artusi A, Debattista K, Chalmers A (2011) Advanced high dynamic range imaging: theory and practice. AK PetersGoogle Scholar
  10. 10.
    Baxes GA (1994) Digital image processing: principles and applications. Wiley, New YorkGoogle Scholar
  11. 11.
    Bennett EP, McMillan L (2005) Video enhancement using per-pixel virtual exposures. In: ACM Transactions on graphics (TOG), ACM, vol 24, pp 845–852Google Scholar
  12. 12.
    Celik T, Tjahjadi T (2011) Contextual and variational contrast enhancement. IEEE Trans Image Process 20(12):3431–3441MathSciNetzbMATHGoogle Scholar
  13. 13.
    Chen CR, Chiu CT, Chang YC (2011) Inverse tone mapping operator evaluation using blind image quality assessment. In: Proceedings of the association annual summit and conference on Asia–Pacific sign and information, APSIPA OctGoogle Scholar
  14. 14.
    Dabov K, Foi A, Katkovnik V, Egiazarian K (2007) Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE Trans Image Process 16 (8):2080–2095MathSciNetGoogle Scholar
  15. 15.
    Deng G (2011) A generalized unsharp masking algorithm. IEEE Trans Image Process 20(5):1249–1261MathSciNetzbMATHGoogle Scholar
  16. 16.
    Durand F, Dorsey J (2002) Fast bilateral filtering for the display of high-dynamic-range images. In: ACM transactions on graphics (TOG), ACM, vol 21, pp 257–266Google Scholar
  17. 17.
    Eisemann E, Durand F (2004) Flash photography enhancement via intrinsic relighting. ACM Trans Graph (TOG) 23(3):673–678Google Scholar
  18. 18.
    Farbman Z, Fattal R, Lischinski D, Szeliski R (2008) Edge-preserving decompositions for multi-scale tone and detail manipulation. In: ACM transactions on graphics (TOG), ACM, vol 27, p 67Google Scholar
  19. 19.
    Fu X, Zeng D, Huang Y, Liao Y, Ding X, Paisley J (2016) A fusion-based enhancing method for weakly illuminated images. Signal Process 129:82–96Google Scholar
  20. 20.
    Gonzalez RC, Woods RE (2007) Image processing. Digital image processing 2Google Scholar
  21. 21.
    Guo X, Li Y, Ling H (2017) Lime: low-light image enhancement via illumination map estimation. IEEE Trans Image Process 26(2):982–993MathSciNetzbMATHGoogle Scholar
  22. 22.
    He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341–2353Google Scholar
  23. 23.
    Huo Y, Yang F, Brost V (2013) Dodging and burning inspired inverse tone mapping algorithm. J Comput Inf Syst 9(9):3461–3468Google Scholar
  24. 24.
    Jobson DJ, Rahman Z u, Woodell GA (1997) Properties and performance of a center/surround retinex. IEEE Trans Image Process 6(3):451–462Google Scholar
  25. 25.
    Kimmel R, Elad M, Shaked D, Keshet R, Sobel I (2003) A variational framework for retinex. Int J Comput Vis 52(1):7–23zbMATHGoogle Scholar
  26. 26.
    Korshunov P, Nemoto H, Skodras A, Ebrahimi T (2014) Crowdsourcing-based evaluation of privacy in hdr images. In: SPIE photonics Europe, international society for optics and photonics, pp 913802–913802Google Scholar
  27. 27.
    Lee C, Lee C, Kim CS (2013) Contrast enhancement based on layered difference representation of 2d histograms. IEEE Trans Image Process 22(12):5372–5384Google Scholar
  28. 28.
    Lee CH, Chen LH, Wang WK (2012) Image contrast enhancement using classified virtual exposure image fusion. IEEE Trans Consum Electron 58(4):1253–1261Google Scholar
  29. 29.
    Lotufo R, Morgan A, Johnson A (1990) Automatic number-plate recognition. In: IEE colloquium on image analysis for transport applications, IET, pp 6–1Google Scholar
  30. 30.
    Mertens T, Kautz J, Van Reeth F (2009) Exposure fusion: a simple and practical alternative to high dynamic range photography. In: Computer graphics forum, Wiley online library, vol 28, pp 161–171Google Scholar
  31. 31.
    Messina G, Castorina A, Battiato S, Bosco A (2003) Image quality improvement by adaptive exposure correction techniques. In: Proceedings of the 2003 international conference on multimedia and expo, 2003. ICME’03, IEEE, vol 1, pp I–549Google Scholar
  32. 32.
    Meylan L, Süsstrunk S (2004) Color image enhancement using a retinex-based adaptive filter. In: Conference on colour in graphics, imaging, and vision, society for imaging science and technology, vol 2004, pp 359–363Google Scholar
  33. 33.
    Meylan L, Daly S, Süsstrunk S (2006) The reproduction of specular highlights on high dynamic range displays. In: Color and imaging conference, society for imaging science and technology, vol 2006, pp 333–338Google Scholar
  34. 34.
    Min D, Choi S, Lu J, Ham B, Sohn K, Do MN (2014) Fast global image smoothing based on weighted least squares. IEEE Trans Image Process 23(12):5638–5653MathSciNetzbMATHGoogle Scholar
  35. 35.
    Mittal A, Soundararajan R, Bovik AC (2013) Making a completely blind image quality analyzer. IEEE Signal Process Lett 20(3):209–212Google Scholar
  36. 36.
    Nayar SK, Mitsunaga T (2000) High dynamic range imaging: spatially varying pixel exposures. In: 2000 Proceedings of the IEEE conference on computer vision and pattern recognition, IEEE, vol 1, pp 472–479Google Scholar
  37. 37.
    Pisano ED, Zong S, Hemminger BM, DeLuca M, Johnston RE, Muller K, Braeuning MP, Pizer SM (1998) Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms. J Digit Imaging 11(4):193–200Google Scholar
  38. 38.
    Pizer SM, Amburn EP, Austin JD, Cromartie R, Geselowitz A, Greer T, ter Haar Romeny B, Zimmerman JB, Zuiderveld K (1987) Adaptive histogram equalization and its variations. Comput Vis Graph Image Process 39(3):355–368Google Scholar
  39. 39.
    Reinhard E, Stark M, Shirley P, Ferwerda J (2002) Photographic tone reproduction for digital images. ACM Trans Graph (TOG) 21(3):267–276Google Scholar
  40. 40.
    Reinhard E, Heidrich W, Debevec P, Pattanaik S, Ward G, Myszkowski K (2010) High dynamic range imaging: acquisition, display, and image-based lighting. Morgan KaufmannGoogle Scholar
  41. 41.
    Rempel AG, Trentacoste M, Seetzen H, Young HD, Heidrich W, Whitehead L, Ward G (2007) Ldr2hdr: on-the-fly reverse tone mapping of legacy video and photographs. In: ACM transactions on graphics (TOG), ACM, vol 26, p 39Google Scholar
  42. 42.
    The photomatix website. (2018)
  43. 43.
    Tsai DY, Lee Y, Matsuyama E (2008) Information entropy measure for evaluation of image quality. J Digit Imaging 21(3):338–347Google Scholar
  44. 44.
    Wang S, Zheng J, Hu HM, Li B (2013) Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Trans Image Process 22(9):3538–3548Google Scholar
  45. 45.
    Wang Y, Zhuo S, Tao D, Bu J, Li N (2013) Automatic local exposure correction using bright channel prior for under-exposed images. Signal Process 93(11):3227–3238Google Scholar
  46. 46.
    Wang TH, Chiu CW, Wu WC, Wang JW, Lin CY, Chiu CT, Liou JJ (2015) Pseudo-multiple-exposure-based tone fusion with local region adjustment. IEEE Trans Multimedia 17(4):470–484Google Scholar
  47. 47.
    Xue W, Zhang L, Mou X, Bovik AC (2014) Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Trans Image Process 23(2):684–695MathSciNetzbMATHGoogle Scholar
  48. 48.
    Yoon DI (2008) Contrast enhancement using brightness preserving bi-histogram equalization. School of Information and Computer EngineeringGoogle Scholar
  49. 49.
    Zhang E, Yang H, Xu M, et al (2015) A novel tone mapping method for high dynamic range image by incorporating edge-preserving filter into method based on retinex. Appl Math Inf Sci 9(1):411–417Google Scholar
  50. 50.
    Zhang K, Zuo W, Chen Y, Meng D, Zhang l (2017) Beyond a gaussian denoiser: residual learning of deep cnn for image denoising. IEEE Transactions on image Processing 26(7):3142–3155MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Samsung ElectronicsGyoengGi-DoKorea
  2. 2.Department of Electrical and Computer Engineering, INMCSeoul National UniversitySeoulKorea

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