An ongoing effort of developing new and implementing established single image highlight removal (SIHR) methods on MATLAB.
I welcome and encourage additions upon review.
Disclaimer: this repository is for educational purposes only.
Run SIHR.m
for path setup.
Run help SIHR
for (minimal) documentation.
The environment this repository is being developed is:
- MATLAB R2019a;
- Image Processing Toolbox.
The repository is structured as follows:
SIHR\
↳ img\
↳ Test images.
↳ Tan2005\
↳ Implementation of Tan's zHighlightRemoval class [3].
Available at (C++):
http://tanrobby.github.io/code/highlight.zip.
↳ Yoon2006\
↳ Implementation of Yoon's 2006 method [4].
↳ Shen2008\
↳ Code for [5].
Also available at (MATLAB):
http://ivlab.org/publications/PR2008_code.zip.
↳ Shen2009\
↳ Code for [6].
↳ Yang2010\
↳ Implementation of Yang's qx_highlight_removal_bf method [7, 10].
Formerly available at (C++):
http://www.cs.cityu.edu.hk/~qiyang/publications/code/eccv-10.zip.
↳ Akashi2015\
↳ Direct implementation of [11].
↳ Yamamoto2017\
↳ Implementation of improvements in [12].
Feel free to create either an issue or a PR or contact me for any questions, comments, or improvements.
Below are listed references for works herein present and a couple survey papers for further reading.
-
A. Artusi, F. Banterle, and D. Chetverikov, “A Survey of Specularity Removal Methods,” Computer Graphics Forum, vol. 30, no. 8, pp. 2208–2230, Aug. 2011 [Online]. Available: http://dx.doi.org/10.1111/J.1467-8659.2011.01971.X;
-
H. A. Khan, J.-B. Thomas, and J. Y. Hardeberg, “Analytical Survey of Highlight Detection in Color and Spectral Images,” in Lecture Notes in Computer Science, Springer International Publishing, 2017, pp. 197–208 [Online]. Available: http://dx.doi.org/10.1007/978-3-319-56010-6_17;
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R. T. Tan and K. Ikeuchi, “Separating reflection components of textured surfaces using a single image,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 2, pp. 178–193, Feb. 2005 [Online]. Available: http://dx.doi.org/10.1109/TPAMI.2005.36;
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K. Yoon, Y. Choi, and I. S. Kweon, “Fast Separation of Reflection Components using a Specularity-Invariant Image Representation,” in 2006 International Conference on Image Processing, 2006 [Online]. Available: http://dx.doi.org/10.1109/ICIP.2006.312650;
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H.-L. Shen, H.-G. Zhang, S.-J. Shao, and J. H. Xin, “Chromaticity-based separation of reflection components in a single image,” Pattern Recognition, vol. 41, no. 8, pp. 2461–2469, Aug. 2008 [Online]. Available: http://dx.doi.org/10.1016/J.PATCOG.2008.01.026;
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H.-L. Shen and Q.-Y. Cai, “Simple and efficient method for specularity removal in an image,” Applied Optics, vol. 48, no. 14, p. 2711, May 2009 [Online]. Available: http://dx.doi.org/10.1364/AO.48.002711;
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R. Grosse, M. K. Johnson, E. H. Adelson, and W. T. Freeman, “Ground truth dataset and baseline evaluations for intrinsic image algorithms,” in 2009 IEEE 12th International Conference on Computer Vision, 2009 [Online]. Available: http://dx.doi.org/10.1109/ICCV.2009.5459428;
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Q. Yang, S. Wang, and N. Ahuja, “Real-Time Specular Highlight Removal Using Bilateral Filtering,” in Computer Vision – ECCV 2010, Springer Berlin Heidelberg, 2010, pp. 87–100 [Online]. Available: http://dx.doi.org/10.1007/978-3-642-15561-1_7;
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H.-L. Shen and Z.-H. Zheng, “Real-time highlight removal using intensity ratio,” Applied Optics, vol. 52, no. 19, p. 4483, Jun. 2013 [Online]. Available: http://dx.doi.org/10.1364/AO.52.004483;
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Q. Yang, J. Tang, and N. Ahuja, “Efficient and Robust Specular Highlight Removal,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 6, pp. 1304–1311, Jun. 2015 [Online]. Available: http://dx.doi.org/10.1109/TPAMI.2014.2360402;
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Y. Akashi and T. Okatani, “Separation of reflection components by sparse non-negative matrix factorization,” Computer Vision and Image Understanding, vol. 146, pp. 77–85, May 2016 [Online]. Available: http://dx.doi.org/10.1016/j.cviu.2015.09.001;
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T. Yamamoto, T. Kitajima, and R. Kawauchi, “Efficient improvement method for separation of reflection components based on an energy function,” in 2017 IEEE International Conference on Image Processing (ICIP), 2017 [Online]. Available: http://dx.doi.org/10.1109/ICIP.2017.8297078;