Highlights removal using reflected energy and histogram analysis

Javed Khan, Aamir Saeed Malik, Nidal Kamel, Sarat Chandra Dass

Abstract


The occurrence of highlights in digital photography is an unwanted phenomenon in computer vision and image analysis. It overshadows the intrinsic color information of objects in a scene and makes computer vision algorithms to produce erroneous results. In this paper, two techniques are proposed; one for removing highlights due to both diffuse and specular components and the other one for removing highlights due to the specular component only. The former is based on the modified Torrance-Sparrow model of specular reflection while the later technique combines Torrance-Sparrow model with local statistical analysis and recovers the color of highlights by interpolation. The results are validated on both synthesized and real images and compared with several contemporary highlights removal techniques. The comparison shows that performance of the proposed techniques is comparable or slightly better than the previously proposed techniques.


Keywords


Specular reflection, diffuse reflection, statistical analysis, local information, highlights

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References


Artusi A, Banterle F and Chetverikov D. 2011. A survey of specularity removal methods. Computer Graphics Forum. Wiley Online Library, 2208-2230.

Bajcsy R, Lee SW and Leonardis A. 1996. Detection of diffuse and specular interface reflections and inter-reflections by color image segmentation. International Journal of Computer Vision 17: 241-272.

Barsky S and Petrou M. 2003. The 4-source photometric stereo technique for three-dimensional surfaces in the presence of highlights and shadows. IEEE Transactions on pattern analysis and machine intelligence 25: 1239-1252.

Bronstein AM, Bronstein MM, Zibulevsky M, et al. 2003. Blind separation of reflections using sparse ICA. Proc. Int. Conf. ICA2003, Nara, Japan. 227-232.

Feris R, Raskar R, Tan K-H, et al. 2004. Specular reflection reduction with multi-flash imaging. Computer Graphics and Image Processing, 2004. Proceedings. 17th Brazilian Symposium on. IEEE, 316-321.

Khan J, Malik AS, Kamel N, et al. 2015. Segmentation of acne lesion using fuzzy C-means technique with intelligent selection of the desired cluster. Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE. IEEE, 3077-3080.

Kim DW, Lin S, Hong K-S, et al. 2002. Variational Specular Separation Using Color and Polarization. MVA. 176-179.

Klinker GJ, Shafer SA and Kanade T. 1988. The measurement of highlights in color images. International Journal of Computer Vision 2: 7-32.

Koirala P, Pant P, Hauta-Kasari M, et al. 2011. Highlight detection and removal from spectral image. JOSA A 28: 2284-2291.

Lee H-C. 1986. Method for computing the scene-illuminant chromaticity from specular highlights. JOSA A 3: 1694-1699.

Lee SW and Bajcsy R. 1992. Detection of specularity using colour and multiple views. Image and Vision Computing 10: 643-653.

Lin S, Li Y, Kang SB, et al. 2002. Diffuse-specular separation and depth recovery from image sequences. European conference on computer vision. Springer, 210-224.

Lin S, Quan L and Shum H-Y. 2003. Highlight removal by illumination-constrained inpainting. Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on. IEEE, 164-169.

Lin S and Shum H-Y. 2001. Separation of diffuse and specular reflection in color images. Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on. IEEE, I-I.

Mallick SP, Zickler T, Belhumeur PN, et al. 2006. Specularity removal in images and videos: A PDE approach. European Conference on Computer Vision. Springer, 550-563.

Nayar SK, Fang X-S and Boult T. 1997. Separation of reflection components using color and polarization. International Journal of Computer Vision 21: 163-186.

Nayar SK, Ikeuchi K and Kanade T. 1989. Surface reflection: physical and geometrical perspectives. DTIC Document.

Sato Y and Ikeuchi K. 1994. Temporal-color space analysis of reflection. JOSA A 11: 2990-3002.

Schechner YY, Shamir J and Kiryati N. 2000. Polarization and statistical analysis of scenes containing a semireflector. JOSA A 17: 276-284.

Shafer SA. 1985. Using color to separate reflection components. Color Research & Application 10: 210-218.

Shen H-L, Zhang H-G, Shao S-J, et al. 2008. Chromaticity-based separation of reflection components in a single image. Pattern recognition 41: 2461-2469.

Suo J, An D, Ji X, et al. 2016. Fast and High Quality Highlight Removal From a Single Image. IEEE Transactions on Image Processing 25: 5441-5454.

Tan RT and Ikeuchi K. 2005. Separating reflection components of textured surfaces using a single image. IEEE Transactions on pattern analysis and machine intelligence 27: 178-193.

Tan RT, Nishino K and Ikeuchi K. 2004. Color constancy through inverse-intensity chromaticity space. JOSA A 21: 321-334.

Tominaga S. 1991. Surface identification using the dichromatic reflection model. IEEE Transactions on pattern analysis and machine intelligence 13: 658-670.

Tominaga S and Tanaka N. 2003. Refractive index estimation and color image rendering. Pattern recognition letters 24: 1703-1713.

Tominaga S and Wandell BA. 1989. Standard surface-reflectance model and illuminant estimation. JOSA A 6: 576-584.

Torrance KE and Sparrow EM. 1967. Theory for off-specular reflection from roughened surfaces. JOSA 57: 1105-1114.

Umeyama S and Godin G. 2004. Separation of diffuse and specular components of surface reflection by use of polarization and statistical analysis of images. IEEE Transactions on pattern analysis and machine intelligence 26: 639-647.

Wolff LB. 1990. Polarization-based material classification from specular reflection. IEEE Transactions on pattern analysis and machine intelligence 12: 1059-1071.


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