Abstract
We propose a robust multimodal dictionary learning method for multimodal images. Joint dictionary learning for both modalities may be impaired by lack of correspondence between image modalities in training data, for example due to areas of low quality in one of the modalities. Dictionaries learned with such non-corresponding data will induce uncertainty about image representation. In this paper, we propose a probabilistic model that accounts for image areas that are poorly corresponding between the image modalities. We cast the problem of learning a dictionary in presence of problematic image patches as a likelihood maximization problem and solve it with a variant of the EM algorithm. Our algorithm iterates identification of poorly corresponding patches and refinements of the dictionary. We tested our method on synthetic and real data. We show improvements in image prediction quality and alignment accuracy when using the method for multimodal image registration.
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Aharon, M., Elad, M., Bruckstein, A.: K-svd: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing 54(11), 4311–4322 (2006)
Besag, J.: On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society. Series B (Methodological), 259–302 (1986)
Cao, T., Zach, C., Modla, S., Powell, D., Czymmek, K., Niethammer, M.: Registration for correlative microscopy using image analogies. Biomedical Image Registration, 296–306 (2012)
Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing 15(12), 3736–3745 (2006)
Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online dictionary learning for sparse coding. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 689–696. ACM (2009)
Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Supervised dictionary learning. In: NIPS, pp. 1033–1040 (2008)
Monaci, G., Jost, P., Vandergheynst, P., Mailhe, B., Lesage, S., Gribonval, R.: Learning multimodal dictionaries. IEEE Transactions on Image Processing 16(9), 2272–2283 (2007)
Neal, R., Hinton, G.: A view of the em algorithm that justifies incremental, sparse, and other variants. NATO ASI Series D Behavioural and Social Sciences 89, 355–370 (1998)
Roy, S., Carass, A., Prince, J.: A compressed sensing approach for mr tissue contrast synthesis. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 371–383. Springer, Heidelberg (2011)
Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(2), 210–227 (2009)
Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution via sparse representation. IEEE Transactions on Image Processing 19(11), 2861–2873 (2010)
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Cao, T., Jojic, V., Modla, S., Powell, D., Czymmek, K., Niethammer, M. (2013). Robust Multimodal Dictionary Learning. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8149. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40811-3_33
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DOI: https://doi.org/10.1007/978-3-642-40811-3_33
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