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Image warp preserving content intensity. (English) Zbl 1499.68376

Summary: An accurate method for warping images is presented. Different from most commonly used techniques, this method guarantees the conservation of the intensity of the transformed image, evaluated as the sum of its pixel values over the whole image or over corresponding transformed subregions of it. Such property is mandatory for quantitative analysis, as, for instance, when deformed images are used to assess radiances, to measure optical fluxes from light sources, or to characterize material optical densities. The proposed method enforces area resampling by decomposing each rectangular pixel into two triangles, and projecting the pixel intensity onto half pixels of the transformed image, with weights proportional to the area of overlap of the triangular half-pixels. The result is quantitatively exact, as long as the original pixel value is assumed to represent a constant image density within the pixel area, and as long as the coordinate transformation is diffeomorphic. Implementation details and possible variations of the method are discussed.

MSC:

68U10 Computing methodologies for image processing
54H30 Applications of general topology to computer science (e.g., digital topology, image processing)
94A08 Image processing (compression, reconstruction, etc.) in information and communication theory

References:

[1] A. Amanatiadis and I. Andreadis, A survey on evaluation methods for image interpolation, Measurement Science and Technology, 20 (2009), 104015, https://doi.org/10.1088/0957-0233/20/10/104015.
[2] M. F. Beg, M. I. Miller, A. Trouvé, and L. Younes, Computing large deformation metric mappings via geodesic flows of diffeomorphisms, Int. J. Comput. Vis., 61 (2005), pp. 139-157, https://doi.org/10.1023/B:VISI.0000043755.93987.aa. · Zbl 1477.68459
[3] T. Beier and S. Neely, Feature-based image metamorphosis, SIGGRAPH Comput. Graph., 26 (1992), pp. 35-42, https://doi.org/10.1145/142920.134003.
[4] G. Bradski, The OpenCV Library, Dr. Dobb’s Journal of Software Tools, 120 (2000), pp. 122-125.
[5] M.-C. Chiang, Imaging-Consistent Warping and Super-resolution, Ph.D. thesis, Columbia University, 1998, https://www.proquest.com/docview/304435854.
[6] H. S. M. Coxeter, Introduction to Geometry, 2nd ed., Wiley, New York, 1969. · Zbl 0181.48101
[7] A. H. Elsheikh and M. Elsheikh, A reliable triangular mesh intersection algorithm and its application in geological modelling, Engineering with Computers, 30 (2014), pp. 143-157, https://doi.org/10.1007/s00366-012-0297-3.
[8] C. Ericson, Real-Time Collision Detection, Morgan Kaufmann Series in interactive 3D Technology, CRC Press, London, 2005, https://learning.oreilly.com/library/view/real-time-collision-detection/9781558607323/.
[9] K. M. Fant, A nonaliasing, real-time spatial transform technique, IEEE Computer Graphics and Applications, 6 (1986), pp. 71-80, https://doi.org/10.1109/mcg.1986.276613.
[10] E. Fogel, O. Setter, R. Wein, G. Zucker, B. Zukerman, and D. Halperin, 2D regularized boolean set-operations, in CGAL User and Reference Manual, 5.3, CGAL Editorial Board, 2021, https://doc.cgal.org/5.3/Manual/packages.html#PkgBooleanSetOperations2.
[11] A. S. Fruchter and R. N. Hook, Drizzle: A method for the linear reconstruction of undersampled images, Publications of the Astronomical Society of the Pacific, 114 (2002), pp. 144-152, https://doi.org/10.1086/338393.
[12] P. Getreuer, Linear methods for image interpolation, IPOL J. Image Process. Online, 1 (2011), pp. 238-259, https://doi.org/10.5201/ipol.2011.g_lmii. · Zbl 1235.65012
[13] D. Ghosh and N. Kaabouch, A survey on image mosaicing techniques, J. Visual Communication and Image Representation, 34 (2016), pp. 1-11, https://doi.org/10.1016/j.jvcir.2015.10.014.
[14] C. A. Glasbey and K. V. Mardia, A review of image-warping methods, J. Appl. Stati., 25 (1998), pp. 155-171, https://doi.org/10.1080/02664769823151. · Zbl 0935.62072
[15] P. Guigue and O. Devillers, Fast and robust triangle-triangle overlap test using orientation predicates, J. Graphics Tools, 8 (2003), pp. 25-32, https://doi.org/10.1080/10867651.2003.10487580.
[16] D. Han, Real-time digital image warping for display distortion correction, in Image Analysis and Recognition, M. Kamel and A. Campilho, eds., Springer, Berlin, 2005, pp. 1258-1265, https://doi.org/10.1007/11559573_152.
[17] P. S. Heckbert, Fundamentals of Texture Mapping and Image Warping, Master’s thesis, Department of Electrical Engineering and Computer Science, University of California, Berkeley, 1989, http://www.cs.cmu.edu/ ph/texfund/texfund.pdf.ļearpage
[18] ImageMagick, Version 7.0.10, ImageMagick Development Team, 2021, https://imagemagick.org (accessed 2021-01-04).
[19] C. Jules, Accurate point in triangle test, 2014, http://totologic.blogspot.com/2014/01/accurate-point-in-triangle-test.html.
[20] S. Lee, G. Wolberg, and S. Y. Shin, Polymorph: morphing among multiple images, IEEE Computer Graphics and Applications, 18 (1998), pp. 58-71, https://doi.org/10.1109/38.637304.
[21] L. Middleton and J. Sivaswamy, Hexagonal Image Processing, Springer-Verlag, Berlin, 2005, https://doi.org/10.1007/1-84628-203-9. · Zbl 1083.68632
[22] P. Milanfar, ED., Super-Resolution Imaging, CRC Press, Boca Raton, FL, 2011, https://doi.org/10.1201/9781439819319.
[23] J. Modersitzki, Numerical Methods for Image Registration, Numer. Math. Sci. Comput., Oxford University Press, Oxford, UK, 2004. · Zbl 1055.68140
[24] J. Modersitzki, FAIR: Flexible Algorithms for Image Registration, SIAM, Philadelphia, 2009, https://doi.org/10.1137/1.9780898718843.ch3. · Zbl 1183.68695
[25] T. Möller, A fast triangle-triangle intersection test, J. Graphics Tools, 2 (2004), pp. 25-30, https://doi.org/10.1080/10867651.1997.10487472.
[26] J. A. Parker, R. V. Kenyon, and D. E. Troxel, Comparison of interpolating methods for image resampling, IEEE Trans. Medical Imaging, 2 (1983), pp. 31-39, https://doi.org/10.1109/TMI.1983.4307610.
[27] R. J. Radke, Computer Vision for Visual Effects, Cambridge University Press, New York, 2012, https://doi.org/10.1017/CBO9781139019682.
[28] M. Reinelt, pamscale, in Netpbm User Manual, 2020, http://netpbm.sourceforge.net/doc/pamscale.html.
[29] C. L. Sabharwal and J. L. Leopold, A triangle-triangle intersection algorithm, in Computer Science & Information Technology, 2015, https://doi.org/10.5121/csit.2015.51003.
[30] M. L. Sampoli, An Automatic Procedure to Compute Efficiently the Intersection of Two Triangles, Tech. Report 465, Dipartimento di Scienze Matematiche ed Informatiche, Università di Siena, 2004, https://www.researchgate.net/publication/265809721_An_Automatic_Procedure_to_Compute_Efficiently_the_Intersection_of_Two_Triangles.
[31] J. Summers, Pixel Mixing, Image Worsener, 2012, http://entropymine.com/imageworsener/pixelmixing/.
[32] H. Takeda, S. Farsiu, and P. Milanfar, Kernel regression for image processing and reconstruction, IEEE Trans. Image Processing, 16 (2007), pp. 349-366, https://doi.org/10.1109/TIP.2006.888330.
[33] A. Thyssen, ImageMagick v6 Examples – Distorting Images, 2012, http://www.imagemagick.org/Usage/distorts/#area_resample.
[34] O. Tropp, A. Tal, and I. Shimshoni, A fast triangle to triangle intersection test for collision detection, Computer Animation and Virtual Worlds, 17 (2006), pp. 527-535, https://doi.org/10.1002/cav.115.
[35] S. van der Walt, J. L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, and T. Yu, scikit-image: image processing in Python, PeerJ, 2 (2014), e453, https://doi.org/10.7717/peerj.453.
[36] L. Velho, A. Frery, and J. Gomes, Warping and morphing, in Image Processing for Computer Graphics and Vision, Springer London, London, 2009, pp. 387-412, https://doi.org/10.1007/978-1-84800-193-0_15. · Zbl 1162.68795
[37] A. G. Weber, The USC-SIPI Image Database: Version 6, 2018, http://sipi.usc.edu/database/SIPI_Database.pdf.
[38] G. Wolberg, Digital Image Warping, IEEE Computer Society Press, Los Alamitos, CA, 1990.
[39] O. Yadid-Pecht and R. Etienne-Cummings, CMOS Imagers, 2004, https://doi.org/10.1007/b117398.
[40] F. Yamaguchi, A unified approach to interference problems using a triangle processor, SIGGRAPH Comput. Graph., 19 (1985), pp. 141-149, https://doi.org/10.1145/325165.325224.
[41] X. Yang, R. Kwitt, M. Styner, and M. Niethammer, Quicksilver: Fast predictive image registration-a deep learning approach, NeuroImage, 158 (2017), pp. 378-396, https://doi.org/10.1016/j.neuroimage.2017.07.008.
[42] B. Zackay and E. O. Ofek, How to COAAD images. I. Optimal source detection and photometry of point sources using ensembles of images, Astrophys. J., 836 (2017), 187, https://doi.org/10.3847/1538-4357/836/2/187.
[43] S. Zhang, P. X. Liu, M. Zheng, and W. Shi, A diffeomorphic unsupervised method for deformable soft tissue image registration, Comput. Biol. Med., 120 (2020), 103708, https://doi.org/10.1016/j.compbiomed.2020.103708.
[44] B. Zitová and J. Flusser, Image registration methods: A survey, Image Vision Computing, 21 (2003), pp. 977-1000, https://doi.org/10.1016/S0262-8856(03)00137-9.
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