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Convolutional sparse representations are a form of sparse representation with a dictionary that has a structure that is equivalent to convolution with a set of linear filters.
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B. Wohlberg, “Convolutional sparse representations as an image model for impulse noise restoration,” in Proc. IEEE Image, Video Multidim. Signal Process. Workshop (IVMSP) , Bordeaux, France, Jul. 2016. doi: 10.1109/IVMSPW.2016.7528229
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2015
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F. Heide, W. Heidrich, and G. Wetzstein, “Fast and flexible convolutional sparse coding,” in Proc. IEEE Conf. Comp. Vis. Pat. Recog. (CVPR) , 2015, pp. 5135–5143. doi: 10.1109/CVPR.2015.7299149
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S. Gu, W. Zuo, Q. Xie, D. Meng, X. Feng, and L. Zhang, “Convolutional sparse coding for image super-resolution,” in Proc. IEEE Intl. Conf. Comput. Vis. (ICCV) , Dec. 2015. doi: 10.1109/ICCV.2015.212
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Y. Liu, X. Chen, R. K. Ward, and Z. J. Wang, “Image fusion with convolutional sparse representation,” IEEE Signal Process. Lett. , 2016. doi: 10.1109/lsp.2016.2618776
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T. M. Quan and W.-K. Jeong, “Compressed sensing reconstruction of dynamic contrast enhanced MRI using GPU-accelerated convolutional sparse coding,” in IEEE Intl. Symp. Biomed. Imag. (ISBI) , Apr. 2016, pp. 518–521. doi: 10.1109/ISBI.2016.7493321
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2017
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2017
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C. Garcia-Cardona and B. Wohlberg, “Subproblem coupling in convolutional dictionary learning,” in Proc. IEEE Conf. Image Process. (ICIP) , Beijing, China, Sep. 2017, pp. 1697–1701. doi: 10.1109/ICIP.2017.8296571
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2017
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I. Y. Chun and J. A. Fessler, “Convolutional dictionary learning: Acceleration and convergence,” IEEE Trans. Image Process. , vol. 27, no. 4, pp. 1697–1712, Apr. 2018. doi: 10.1109/TIP.2017.2761545
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2018
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