Image enhancement method based on multi algorithm fusion

Authors

  • Xinyu Gao

DOI:

https://doi.org/10.54097/hset.v60i.10338

Keywords:

fusion, multi-scale, adaptive image enhancement, PCA

Abstract

This research suggests an image enhancement method based on multi algorithm fusion. The illumination component and reflection component are obtained by morphological closure. On the one hand, multiple inputs of the estimated illumination and the corresponding weights are fused by multi-scale fusion technology. On the other hand, space conversion technology is used. Two adjusted images are obtained based on multi-scale Gaussian filter and adaptive brightness enhancement algorithm. Then details are extracted from these two images through PCA fusion strategy. The reflection component is improved significantly after above operations. Finally, combine the illumination and reflection components. Compared with the classical algorithm, our method can simultaneously achieve brightness improvement, naturalness maintenance and contrast enhancement.

Downloads

Download data is not yet available.

References

T. Wang, Z. Ji, and Q. Sun, et al, “Label propagation and higher-order constraint-based segmentation of fluid-associated regions in retinal SD-OCT images,” Inform. Sci. 358, pp. 92–111, 2016.

Z. Zhou, S. Li, and B. Wang, “Multi-scale weighted gradient-based fusion for multi focus images,” Inf. Fusion 20, pp. 60–72, 2014.

T. Mertens, J. Kautz, and F.V. Reeth, “Exposure fusion, in: Proceedings of 15th Pacifific Conference on Computer Graphics and Applications,” pp. 382–390, 2007.

Z. G. Li, J. H. Zheng, and S. Rahardja, “Detail-enhanced exposure fusion,” IEEE Trans. Image Process, vol. 21, no. 11, pp. 4672–4676, 2012.

C. Lee, C. Lee, and C. S. Kim, “Contrast enhancement based on layered difference representation of 2d histograms,” IEEE Trans. Image Process, vol. 22, no. 12, pp. 5372–5384, 2013.

R. C. Gonzalez and R. E. Woods, “Digital Image Processing,” Prentice-Hall, Englewood Cliffs, NJ, USA, 2007.

E. D. Pisano, S. Zong, B. M. Hemminger, “Contrast limited adaptive histogram image processing to improve the detection of simulated spiculations in dense mammograms, ” J. Digit. Imaging, vol. 11, no. 4, pp. 193-200, 1998.

Y.T. Kim, “Contrast enhancement using brightness preserving bi-histogram equalization,” IEEE Trans. Consum. Electron, vol. 43, no. 1, pp. 1–8, 1997.

D. Coltuc, P. Bolon, J. M. Chassery, “Exact histogram specifification,” IEEE Trans. Image Process, vol. 15, no. 5, pp. 1143–1152, 2006.

T. Arici, S. Dikbas, and Y. Altunbasak, “A histogram modifification framework and its application for image contrast enhancement,” IEEE Trans. Image Process, vol. 18, no. 9, pp. 1921–1935, 2009.

T. Celik and Tjahjadi, “Contextual and variational contrast enhancement,” IEEE Trans. Image Process, vol. 20, no. 12, pp. 3431–3441, 2011.

H. Xu, G. Zhai, X. Wu, and X. Yang, “Generalized equalization model for image enhancement,” IEEE Trans. Multimed, vol. 16, no. 1, pp. 68–82, 2014.

C. Lee, C. Lee, and Y.Y. Lee, “Power-constrained contrast enhancement for emissive displays based on histogram equalization,” IEEE Trans. Image Process, vol.21, no.1, pp. 80–93, 2012.

T. Celik, “Spatial entropy-based global and local image contrast enhancement,” IEEE Trans. Image Process, vol.23, no. 12, pp. 5298–5308, 2014.

Al-Hashim, Mohammad Abid, Al-Ameen, and Zohair, “Retinex-Based Multiphase Algorithm for Low-Light Image Enhancement,” Traitement du Signal: signal image parole, vol. 37, no. 5, pp. 733-743, 2022

M. D. Li, J. Y. Liu, W. H. Yang, X. Y. Sun, and Z. M. Guo, “Structure-Revealing Low-Light Image Enhancement Via Robust Retinex Model,” IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol. 27, no. 6, 2018.

D. J. Jobson, Z. U. Rahman, and G. A. Woodell, “A multi-scale Retinex for bridging the gap between color images and the human observation of scenes,” IEEE Trans. Image Process, vol. 6, no. 7, pp. 965–976, 1997.

R. Kimmel, M. Elad, and D. Shaked, “A variational framework for Retinex,” Int. J. Comput. Vis, vol. 52, no. 1, pp. 7–23, 2003.

M. K. Ng and W. Wang, “A total variation model for Retinex,” SIAM J. Imaging Sci, vol. 4, no. 1, pp. 345–365, 2003.

Q. Shan, J. Jia, and M. S. Brown, “Globally optimized linear windowed tone mapping,” IEEE Trans. Vis. Comput. Graph, vol. 16, no. 4, pp. 663–675, 2010.

G. Deng, “A generalized unsharp masking algorithm,” IEEE Trans. Image Process, vol. 20, no.5, pp.1249–1261, 2011.

L. Yuan, J. Sun, “Automatic exposure correction of consumer photographs, in:

Proceedings of European Conference on Computer Vision,” pp. 771–785, 2012.

E. Eisemann, F. Durand, “Flash photography enhancement via intrinsic relighting,” ACM Trans. Graph (Proc. SIGGRAPH), vol. 23, no.3, pp. 673–678, 2004.

E.P. Bennett and L. McMillan, “Video enhancement using per-pixel virtual exposures,” ACM Trans. Graph (Proc. SIGGRAPH), vol. 24, no. 3, pp. 845–852, 2005.

R. Fattal, D. Lischinski, and M. Werman, “Gradient domain high dynamic range

compression,” ACM Trans. Graph (Proc. SIGGRAPH), vol. 21, no. 3, pp. 249–256, 2002.

Y. Wang, S. Zhuo, D. Tao, J. Bu, and N. Li, “Automatic local exposure correction using bright channel prior for under-exposed images,” Signal Process, vol. 93, no. 11, pp. 3227–3238, 2013.

E.H. Land and J.J. McCann, “Lightness and Retinex theory,” J. Opt. Soc. Am, vol. 61, no. 1, pp. 1–11, 1971.

E. Land, “The Retinex,” Aiiler. Scient. Jun, vo1, 52, no. 2, pp. 247-264, 1964.

E. Land and J. McCann, “Lightness and Retinex theory,” J. Opt. Soc. Artier. Jan, vol. 61, no. 1, pp.1-11, 1971.

R. Kimmel,M. Elad,D. Shaked,and R. Keshet, “Sobel. A variational framework for Retinex,” Int. J. Comput. Vis, vol. 52, no. 1, pp.7-23, 2003.

A. Hurlbert, “Formal connections between lightness algorithm,” J. Opt. Soc. Amer, vol. 3, pp. 1 684-1 692, 1986.

J. Frankle and J. McCann, “Method and apparatus for lightness imaging,” US Patent, no. 4, 1983.

B. Funt, F. Ciurea, and J. McCann, “Retinex in matlab,” J. Electron. Imag. Jan, vol. 13, no. 1, pp. 48-57, 2004.

K. He, J. Sun, and X. Tang, “Single image haze removel using dark channel prior,” IEEE Trans. Pattern Anal. Mach. Intell, vol. 33, no. 12, pp. 2341 - 2353, 2011.

E. H. L and J. J. McCann, “Lightness and Retinex theory,” J. Opt. Soc. Am, vol. 61, no. 1, pp. 1-11, 1971.

Downloads

Published

25-07-2023