De-ghosting in High Dynamic Range Imaging Based on Intensity Scaling Cue

doi:10.4316/AECE.2020.03001

3/2020 - 1

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De-ghosting in High Dynamic Range Imaging Based on Intensity Scaling Cue

SHIM, S.-O , ALHARBI, S. , KHAN, I. R. , AZIZ, W.

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Author keywords
image sequence analysis, image fusion, image reconstruction, image motion analysis, image quality

References keywords
dynamic(26), range(20), high(18), image(17), images(15), comput(13), imaging(12), tone(10), process(10), pattern(9)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2020-08-31
Volume 20, Issue 3, Year 2020, On page(s): 3 - 10
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.03001
Web of Science Accession Number: 000564453800001
SCOPUS ID: 85090343221

Abstract

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A High Dynamic Range (HDR) image produced from a sequence of low dynamic range (LDR) images can contain motion artefacts (ghosting) if the scene contains moving objects. Conventional de-ghosting methods first detect moving objects in the scene, and then either remove those moving objects totally or reconstruct them. However, these methods are computationally expensive. This paper proposes a de-ghosting method that does not require explicit detection of moving regions. First, the ratio between camera exposure times of a target image and a reference image, which is called the intensity scaling factor in this paper, is computed. Since the information about camera exposure time is not available always, we propose a novel method to estimate the intensity scaling factor from non-saturated and non-moving pixels. Then, the estimated scaling factor is used as a cue to label every pixel in the target image as either static or moving pixel. Finally, the values of moving pixels are corrected with their expected values which can be estimated from the intensity scaling factor. Experimental results show that the proposed method generates more accurate ghost-free HDR images than the existing state of the art methods.

References

Cited By «-- Click to see who has cited this paper

[1] F. Banterle, A. Artusi, K. Debattista, A. Chalmers, Advanced High Dynamic Range Imaging: Theory and Practice. Boca Raton, FL, USA: CRC Press, 2017.
[CrossRef] [SCOPUS Times Cited 251]

[2] S. K. Nayar, T. Mitsunaga, "High dynamic range imaging: Spatially varying pixel exposures," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 472-479, June 2000.
[CrossRef] [SCOPUS Times Cited 506]

[3] M. A. Robertson, S. Borman, R. L. Stevenson, "Estimation-theoretic approach to dynamic range enhancement using multiple exposures," J. Electron. Imaging, vol. 12, no. 2, pp. 219-229, April 2003.
[CrossRef] [Web of Science Times Cited 156] [SCOPUS Times Cited 205]

[4] Z. Li and J. Zheng, "Visual-salience-based tone mapping for high dynamic range images," IEEE Trans. Ind. Electron., vol. 61, no. 12, pp. 7076-7082, March 2014.
[CrossRef] [Web of Science Times Cited 61] [SCOPUS Times Cited 72]

[5] B. K. Kim, R. H. Park, S. Chang, "Tone mapping with contrast preservation and lightness correction in high dynamic range imaging," Signal, Image and Video Processing, vol. 10, no. 8, pp. 1425-1432, July 2016.
[CrossRef] [Web of Science Times Cited 17] [SCOPUS Times Cited 17]

[6] S. Ferradans, M. Bertalmio, E. Provenzi, V. Caselles, "An analysis of visual adaptation and contrast perception for tone mapping," IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 10, pp. 2002-2012, March 2011.
[CrossRef] [Web of Science Times Cited 86] [SCOPUS Times Cited 97]

[7] K. Kim, J. Bae, J. Kim, "Natural HDR image tone mapping based on retinex", IEEE Trans. Consum. Electron., vol. 57, no. 4, pp. 1807-1814, November 2011.
[CrossRef] [Web of Science Times Cited 48] [SCOPUS Times Cited 61]

[8] I. R. Khan, S. Rahardja, M. M. Khan, M. M. Movania, F. Abed, "A tone-mapping technique based on histogram using a sensitivity model of the human visual system," IEEE Trans. Ind. Electron., vol. 65, no. 4, pp. 3469-3479, October 2017.
[CrossRef] [Web of Science Times Cited 77] [SCOPUS Times Cited 88]

[9] C. Jung, T. Sun, "Optimized perceptual tone mapping for contrast enhancement of images," IEEE Trans. Circuits Syst. Video Technol., vol. 27, no. 6, pp.1161-1170, February 2016.
[CrossRef] [Web of Science Times Cited 27] [SCOPUS Times Cited 33]

[10] G. Eilertsen, R. K. Mantiuk, and J. Unger, "A comparative review of toneâmapping algorithms for high dynamic range video," Comput. Graph. Forum, vol. 36, no. 2, pp. 565-592, May 2017.
[CrossRef] [Web of Science Times Cited 67] [SCOPUS Times Cited 79]

[11] G. Yue, C. Hou, K. Gu, S. Mao, W. Xhang, "Biologically inspired blind quality assessment of tone-mapped images," IEEE Trans. Ind. Electron., vol. 65, no. 3, pp.2525-2536, March 2018.
[CrossRef] [Web of Science Times Cited 56] [SCOPUS Times Cited 64]

[12] G. Yue, C. Hou, T. Zhou, "Blind quality assessment of tone-mapped images considering colorfulness, naturalness and structure," IEEE Trans. Ind. Electron., vol. 66 no. 5, pp. 3784-3793, July 2018.
[CrossRef] [Web of Science Times Cited 55] [SCOPUS Times Cited 60]

[13] H. Yeganeh, Z. Wang, "Objective Quality Assessment of Tone-Mapped Images," IEEE Trans. Image Process., vol. 22, no. 2, pp. 657-667, February 2013.
[CrossRef] [Web of Science Times Cited 455] [SCOPUS Times Cited 537]

[14] E. A. Khan, A. O. Akyuz, E. Reinhard, "Ghost Removal in High Dynamic Range Images," in Proc. IEEE Int. Conf. Image Process., pp. 2005-2008, October 2006.
[CrossRef] [Web of Science Times Cited 143] [SCOPUS Times Cited 196]

[15] O. T. Tursun, A. O. Akyuz, A. Erdem, E. Erdem, "The state of the art in HDR deghosting: a survey and evaluation," Comput. Graph. Forum, vol. 34, no. 2, pp. 683-707, June 2015.
[CrossRef] [Web of Science Times Cited 80] [SCOPUS Times Cited 101]

[16] M. Granados, H. P. Seidel, H. Lensch, "Background estimation from non-time sequence images," in Proc. Graphics & Interface, Canadian Information Processing Society, pp. 33-40, May 2008.

[17] A. Srikantha, D. Sidibe, "Ghost detection and removal for high dynamic range images: Recent advances," Signal Process Image., vol. 27, no. 6, pp. 650-662, July 2012,
[CrossRef] [Web of Science Times Cited 82] [SCOPUS Times Cited 105]

[18] S. Silk, J. Lang, "Fast high dynamic range image deghosting for arbitrary scene motion," in Proc. Graphics & Interface, Canadian Information Processing Society, pp. 85-92, May 2012.

[19] W. Zhang, W. K. Cham, "Gradient-directed composition of multi-exposure images," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 530-536, June 2010.
[CrossRef] [Web of Science Times Cited 67] [SCOPUS Times Cited 82]

[20] F. Pece, J. Kautz, "Bitmap movement detection: HDR for dynamic scenes," in IEEE Conf. Visual Media Production, pp. 1-8, November 2010.
[CrossRef] [SCOPUS Times Cited 101]

[21] O. Galo, N. Gelfandz, W. C. Chen, M. Tico, K. Pulli, "Artifact-free high dynamic range imaging," in IEEE Int. Conf. Computational Photography, pp. 1-7, April 2009.
[CrossRef] [SCOPUS Times Cited 206]

[22] K. Jacobs, C. Loscos, G. Ward, "Automatic high-dynamic range image generation for dynamic scenes," IEEE Comput. Graph., vol. 28, no. 2, pp. 84-93, March 2008.
[CrossRef] [Web of Science Times Cited 169] [SCOPUS Times Cited 213]

[23] H. Y. Lin, W. Z. Chang, "High dynamic range imaging for stereoscopic scene representation," in Proc. IEEE Int. Conf. Image Process., pp. 4305-4308, November 2009.
[CrossRef] [SCOPUS Times Cited 43]

[24] D. K. Lee, R. H. Park, S. Chang, "Improved histogram based ghost removal in exposure fusion for high dynamic range images," in Proc. IEEE Int. Symposium Consum. Electron., pp. 586-591, June 2011.
[CrossRef] [SCOPUS Times Cited 7]

[25] T. H. Min, R. H. Park, S. Chang, "Histogram based ghost removal in high dynamic range images," in Proc. IEEE Int. Conf. Multimedia and Expo, pp. 530-533, July 2009.
[CrossRef] [SCOPUS Times Cited 38]

[26] S. Wu, S. Xie, S. Rahardja, Z. Li, "A robust and fast anti-ghosting algorithm for high dynamic range imaging," in Proc. IEEE Int. Conf. Image Process., 2010, pp. 397-400, September 2010.
[CrossRef] [SCOPUS Times Cited 34]

[27] W. Zhang, W. K. Cham, "Reference-guided exposure fusion in dynamic scenes," J. Vis. Commun. Image. R., vol. 23, no. 3, pp. 467-475, April 2012.
[CrossRef] [Web of Science Times Cited 45] [SCOPUS Times Cited 56]

[28] T. H. Oh, J. Y. Lee, I. S. Kweon, "High dynamic range imaging by a rank-1 constraint," in Proc. IEEE Int. Conf. Image Process., pp. 790-794, September 2013.
[CrossRef] [SCOPUS Times Cited 24]

[29] M. Granados, K. I. Kim, J. Tompkin, C. Theobalt, "Automatic noise modeling for ghost-free HDR reconstruction," ACM Trans. Graphics, vol. 32, no. 6, p. 201, November 2013.
[CrossRef] [Web of Science Times Cited 53] [SCOPUS Times Cited 64]

[30] C. Wang, C. Tu, "An exposure fusion approach without ghost for dynamic scenes," in Proc. IEEE Int. Congress Image and Signal Processing, vol. 2, pp. 904-909, December 2013.
[CrossRef] [SCOPUS Times Cited 10]

[31] K. R. Prabhakar, R. Arora, A. Swaminathan, K. P. Singh, R. V. Babu, "A fast, scalable, and reliable deghosting method for extreme exposure fusion," in IEEE Int. Conf. Computational Photography, pp. 1-8, May 2019.
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 51]

[32] S. O. Shim, I. R. Khan, "Removal of ghosting artefacts in HDRI using intensity scaling cue," in SIGGRAPH Asia 2017 Technical Briefs, p. 16, November 2017.
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 5]

[33] Q. Yan, D. Gong, P. Zhang, Q. Shi, J. Sun, I. Reid, Y. Zhang, "Multi-Scale Dense Networks for Deep High Dynamic Range Imaging," in Proc. IEEE Winter Conf. Appl. Comput. Vis. (WACV), pp. 41-50, January 2019.
[CrossRef] [Web of Science Times Cited 62] [SCOPUS Times Cited 70]

[34] L. Bogoni, "Extending dynamic range of monochrome and color images through fusion," in Proc. IEEE Int. Conf. Pattern Recognit., vol. 3, pp. 7-12, September 2000.
[CrossRef]

[35] H. Zimmer, A. Bruhn, J. Weickert, "Freehand HDR imaging of moving scenes with simultaneous resolution enhancement," Comput. Graph. Forum, vol. 30, no. 2, pp. 405-414, April 2011.
[CrossRef] [Web of Science Times Cited 149] [SCOPUS Times Cited 165]

[36] T. Jinno, M. Okuda, "Multiple exposure fusion for high dynamic range image acquisition," IEEE Trans. Image Process., vol. 21, no. 1, pp. 358-365, June 2011.
[CrossRef] [Web of Science Times Cited 71] [SCOPUS Times Cited 86]

[37] S. Ferradans, M. Bertalmio, E. Provenzi, V. Caselles, "Generation of HDR images in non-static conditions based on gradient fusion," in VISAPP, pp. 31-37, February 2012.
[CrossRef]

[38] D. Hafner, O. Demetz, J. Weickert, "Simultaneous HDR and optic flow computation," in Proc. IEEE Int. Conf. Pattern Recognit., pp. 2065-2070, August 2014.
[CrossRef] [Web of Science Times Cited 28] [SCOPUS Times Cited 33]

[39] S. Wu, J. Xu, Y. W. Tai, C. K. Tang, "Deep high dynamic range imaging with large foreground motions," in Proc. European Conf. Comput. Vis., pp. 117-132, September 2018.
[CrossRef] [Web of Science Times Cited 171] [SCOPUS Times Cited 62]

[40] S. C. Park, H. H. Oh, J. H. Kwon, S. D. Lee, "Motion artifact-free HDR imaging under dynamic environments," in Proc. IEEE Int. Conf. Image Process., 2011, pp. 353-356, September 2011.
[CrossRef] [SCOPUS Times Cited 9]

[41] J. Hu, O. Gallo, K. Pulli, "Exposure stacks of live scenes with hand-held cameras," in Proc. European Conf. Comput. Vis., pp. 499-512, October 2012.
[CrossRef] [SCOPUS Times Cited 55]

[42] P. Sen, N. K. Kalantari, M. Yaesoubi, S. Darabi, D. B. Goldman, E. Shechtman, "Robust patch-based HDR reconstruction of dynamic scenes," ACM Trans. Graph., vol. 31, no. 6, pp. 203-1, November 2012.
[CrossRef] [Web of Science Times Cited 268] [SCOPUS Times Cited 363]

[43] J. Hu, O. Gallo, K. Pulli, X. Sun, "HDR deghosting: How to deal with saturation," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 1163-1170, June 2013.
[CrossRef] [Web of Science Times Cited 186] [SCOPUS Times Cited 243]

[44] H. Farid, "Blind Inverse Gamma Correction," IEEE Trans. Image Process., vol. 10, no. 10, pp. 1428-1433, October 2001.
[CrossRef] [Web of Science Times Cited 223] [SCOPUS Times Cited 285]

[45] T. Mitsunaga, S. K. Nayar, "Radiometric Self Calibration," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., vol. 1, pp. 374-380, June 1999.
[CrossRef]

[46] M. D. Grossberg, S. K. Nayar, "Determining the Camera Response from Images: What is Knowable?" IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 11, pp. 1455-1467, October 2003.
[CrossRef] [Web of Science Times Cited 224] [SCOPUS Times Cited 284]

[47] M. D. Grossberg, S. K. Nayar, "What is the Space of Camera Response Functions," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., vol. 2, pp. II-602, June 2003.
[CrossRef]

[48] O. T. Tursun, A. O. Akyuz, A. Erdem, E. Erdem, "An objective deghosting quality metric for HDR images," Comput. Graph. Forum, vol. 35, no. 2, pp. 139-152, May 2016.
[CrossRef] [Web of Science Times Cited 64] [SCOPUS Times Cited 71]

[49] Y. Fang, H. Zhu, K. Ma, Z. Wang, "Perceptual quality assessment of HDR deghosting algorithms," in Proc. IEEE Int. Conf. Image Process., pp. 3165-3169, September 2017.
[CrossRef] [SCOPUS Times Cited 8]

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De-ghosting in High Dynamic Range Imaging Based on Intensity Scaling Cue