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Issue No.12 - Dec. (2013 vol.35)
pp: 2904-2915
Li Shen , Inst. for Infocomm Res., Singapore, Singapore
Chuohao Yeo , Inst. for Infocomm Res., Singapore, Singapore
Binh-Son Hua , Dept. of Comput. Sci., Nat. Univ. of Singapore, Singapore, Singapore
ABSTRACT
Intrinsic image decomposition is an important problem that targets the recovery of shading and reflectance components from a single image. While this is an ill-posed problem on its own, we propose a novel approach for intrinsic image decomposition using reflectance sparsity priors that we have developed. Our sparse representation of reflectance is based on a simple observation: Neighboring pixels with similar chromaticities usually have the same reflectance. We formalize and apply this sparsity constraint on local reflectance to construct a data-driven second-generation wavelet representation. We show that the reflectance component of natural images is sparse in this representation. We further propose and formulate a global sparse constraint on reflectance colors using the assumption that each natural image uses a small set of material colors. Using this sparse reflectance representation and the global constraint on a sparse set of reflectance colors, we formulate a constrained $(l_1)$-norm minimization problem for intrinsic image decomposition that can be solved efficiently. Our algorithm can successfully extract intrinsic images from a single image without using color models or any user interaction. Experimental results on a variety of images demonstrate the effectiveness of the proposed technique.
INDEX TERMS
Image color analysis, Wavelet transforms, Image decomposition, Multiresolution analysis, Reflectance, Image edge detection,multiresolution analysis, Intrinsic image decomposition, sparse reconstruction
CITATION
Li Shen, Chuohao Yeo, Binh-Son Hua, "Intrinsic Image Decomposition Using a Sparse Representation of Reflectance", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 12, pp. 2904-2915, Dec. 2013, doi:10.1109/TPAMI.2013.136
REFERENCES
 [1] R. Kimmel, M. Elad, D. Shaked, R. Keshet, and I. Sobel, "A Variational Framework for Retinex," Int'l J. Computer Vision, vol. 52, pp. 7-23, 2003. [2] B.V. Funt, M.S. Drew, and M. Brockington, "Recovering Shading from Color Images," Proc. European Conf. Computer Vision, pp. 124-132, 1992. [3] W. Sweldens, "The Lifting Scheme: A Construction of Second Generation Wavelets," SIAM J. Math. Analysis, vol. 29, pp. 511-546, 1998. [4] I. Omer and M. Werman, "Color Lines: Image Specific Color Representation," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 946-953, 2004. [5] H. Barrow and J. Tenenbaum, "Recovering Intrinsic Scene Characteristics from Images," Computer Vision Systems, pp. 3-26, 1978. [6] Y. Weiss, "Deriving Intrinsic Images from Image Sequences," Proc. IEEE Int'l Conf. Computer Vision, vol. 2, pp. 68-75, 2001. [7] Y. Matsushita, S. Lin, S.B. Kang, and H.-Y. Shum, "Estimating Intrinsic Images from Image Sequences with Biased Illumination," Proc. European Conf. Computer Vision, vol. 2, pp. 274-286, 2004. [8] K. Sunkavalli, W. Matusik, H. Pfister, and S. Rusinkiewicz, "Factored Time-Lapse Video," ACM Trans. Graphics, vol. 26, no. 3, p. 101, 2007. [9] A. Troccoli and P. Allen, "Building Illumination Coherent 3D Models of Large-Scale Outdoor Scenes," Int'l J. Computer Vision, vol. 78, nos. 2-3, pp. 261-280, 2008. [10] E. Land and J. McCann, "Lightness and Retinex Theory," J. Optical Soc. Am. A, vol. 3, pp. 1684-1692, 1971. [11] B.K.P. Horn, Robot Vision. MIT Press, 1986. [12] G.D. Finlayson, S.D. Hordley, and M. Drew, "Removing Shadows from Images Using Retinex," Proc. 10th Color Imaging Conf.: Color Science, Systems, and Applications, pp. 73-79, 2002. [13] M. Bell and W.T. Freeman, "Learning Local Evidence for Shading and Reflectance," Proc. IEEE Int'l Conf. Computer Vision, vol. 1, pp. 670-677, 2001. [14] M.F. Tappen, W.T. Freeman, and E.H. Adelson, "Recovering Intrinsic Images from a Single Image," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 9, pp. 1459-1472, Sept. 2005. [15] M. Tappen, E. Adelson, and W. Freeman, "Estimating Intrinsic Component Images Using Non-Linear Regression," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1992-1999, 2006. [16] L. Shen, P. Tan, and S. Lin, "Intrinsic Image Decomposition with Non-Local Texture Cues," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1-7, 2008. [17] A. Bousseau, S. Paris, and F. Durand, "User-Assisted Intrinsic Images," Proc. ACM Siggraph, pp. 1-10, 2009. [18] R. Fattal, "Edge-Avoiding Wavelets and Their Applications," ACM Trans. Graphics, vol. 28, no. 3, pp. 1-10, Aug. 2009. [19] G. Uytterhoeven and A. Bultheel, "The Red-Black Wavelet Transform," Proc. IEEE Benelux Signal Processing Symp., pp. 191-194, 1998. [20] J. Shi and J. Malik, "Normalized Cuts and Image Segmentation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888-905, Aug. 2000. [21] L. Grady, "Random Walks for Image Segmentation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 11, pp. 1768-1783, Nov. 2006. [22] A. Levin, D. Lischinski, and Y. Weiss, "Colorization Using Optimization," ACM Trans. Graphics, vol. 23, no. 3, pp. 689-694, 2004. [23] X. Liu, L. Wan, Y. Qu, T.-T. Wong, S. Lin, C.-S. Leung, and P.-A. Heng, "Intrinsic Colorization," ACM Trans. Graphics, vol. 27, no. 5, pp. 152:1-152:9, Dec. 2008. [24] R. Grosse, M.K. Johnson, E.H. Adelson, and W.T. Freeman, "Ground-Truth Data Set and Baseline Evaluations for Intrinsic Image Algorithms," Proc. IEEE Int'l Conf. Computer Vision, pp. 2335-2342, 2009. [25] S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, "An Interior-Point Method for Large-Scale L1-Regularized Least Squares," IEEE J. Selected Topics in Signal Processing, vol. 1, no. 4, pp. 606-617, Dec. 2007. [26] T. Goldstein and S. Osher, "The Split Bregman Method for L1 Regularized Problems," SIAM J. Imaging Sciences, vol. 2, no. 2, pp. 323-343, 2009. [27] A. Levin, D. Lischinski, and Y. Weiss, "A Closed-Form Solution to Natural Image Matting," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 2, pp. 228-242, Feb. 2008.