Magnetic Resonance Super-resolution Imaging Measurement with Dictionary-optimized Sparse Learning

[1] Malathi, G., Shanthi, V. (2011). Statistical measurement of ultrasound placenta images complicated by gestational diabetes mellitus using segmentation approach. Journal of Information Hiding and Multimedia Signal Processing, 2 (4), 332-343.Search in Google Scholar

[2] Lee, C.F., Chang, W.T. (2010). Recovery of color images by composed associative mining and edge detection. Journal of Information Hiding and Multimedia Signal Processing, 1 (4), 310-324.Search in Google Scholar

[3] Kaganami, H.G., Ali, S.K. Zou, B. (2011). Optimal approach for texture analysis and classification based on wavelet transform and neural network. Journal of Information Hiding and Multimedia Signal Processing, 2 (1), 33-40.Search in Google Scholar

[4] Hu, W.C., Yang, C.Y., Huang, D.Y., Huang, C.H. (2011). Feature-based face detection against skincolor like backgrounds with varying illumination. Journal of Information Hiding and Multimedia Signal Processing, 2 (2), 123-132.Search in Google Scholar

[5] Peng, C.Y., Li, J.W. (2012). Fast sparse representation model for l1-norm minimisation problem. Electronics Letters, 48 (3), 162-164.10.1049/el.2011.3466Search in Google Scholar

[6] Chavez-Roman, H., Ponomaryov, V. (2014). Super resolution image generation using wavelet domain interpolation with edge extraction via a sparse representation. IEEE Geoscience & Remote Sensing Letters, 11 (10), 1777-1781.10.1109/LGRS.2014.2308905Search in Google Scholar

[7] Engan, K., Aase, S.O., Husey, J.H. (1999). Method of optimal directions for frame design. In IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 5, 2443-2446.Search in Google Scholar

[8] Li, Y., Namburi, P., Yu, Z., Guan, C. (2009). Voxel selection in fMRI data analysis based on sparse representation. IEEE Transactions on Biomedical Engineering, 56 (10), 2439-2452.Search in Google Scholar

[9] Xu, D., Huang, Y., Zeng, Z., Xu, X. (2012). Human gait recognition using patch distribution feature and locality-constrained group sparse representation. IEEE Transactions on Image Processing, 21 (1), 316-326.10.1109/TIP.2011.216095621724511Search in Google Scholar

[10] Rubinstein, R., Bruckstein, A.M., Elad, M. (2010). Dictionaries for sparse representation modeling. Proceedings of the IEEE, 98 (6), 1045-1057.10.1109/JPROC.2010.2040551Search in Google Scholar

[11] Cevher, V., Krause, A. (2011). Greedy dictionary selection for sparse representation. IEEE Journal of Selected Topics in Signal Processing, 5 (5), 979-988.10.1109/JSTSP.2011.2161862Search in Google Scholar

[12] Chen, Y., Nasrabadi, N.M., Trac, D. (2011). Sparse representation for target detection in hyperspectral imagery. IEEE Journal of Selected Topics in Signal Processing, 5 (3), 629-640.10.1109/JSTSP.2011.2113170Search in Google Scholar

[13] Yang, B., Li, S. (2010). Multifocus image fusion and restoration with sparse representation. IEEE Transactions on Instrumentation and Measurement, 59 (4), 884-892.10.1109/TIM.2009.2026612Search in Google Scholar

[14] Ogawa, T., Haseyama, M. (2011). Missing image data reconstruction based on adaptive inverse projection via sparse representation. IEEE Transactions on Multimedia, 13 (5), 974-992.10.1109/TMM.2011.2161760Search in Google Scholar

[15] Zuo, W., Lin, Z. (2011). A generalized accelerated proximal gradient approach for total-variation-based image restoration. IEEE Transaction on Image Processing, 20 (10), 2748-2759.10.1109/TIP.2011.213166521435979Search in Google Scholar

[16] Kang, L. W., Hsu, C.Y., Chen, H.W., Lu, C.S., Lin, C.Y., Pei, S.C. (2011). Feature-based sparse representation for image similarity assessment. IEEE Transactions on Multimedia, 13 (5), 1019-1030.10.1109/TMM.2011.2159197Search in Google Scholar

[17] Gao, S., Tsang, I.W., Chia, L.T. (2010). Kernel sparse representation for image classication and face recognition. Lecture Notes in Computer Science, 63 (14), 1-14.Search in Google Scholar

[18] Yang, J., Wright, J., Huang, T. (2010). Image super-resolution via sparse representation. IEEE Transactions on Image Processing, 19, 2861-2873.10.1109/TIP.2010.205062520483687Search in Google Scholar

[19] Zeyde, R., Elad, M., Protter, M. (2012). On single image scale-up using sparse-representations. Lecture Notes in Computer Science, 6920, 711-730.10.1007/978-3-642-27413-8_47Search in Google Scholar

[20] Chavez-Roman, H., Ponomaryov, V. (2014). Super resolution image generation using wavelet domain interpolation with edge extraction via a sparse representation. IEEE Geoscience & Remote Sensing Letters, 11 (10), 1777-1781.10.1109/LGRS.2014.2308905Search in Google Scholar

[21] Guo, Y., Ruan, S., Landre, J., Constans, J.M. (2011). A sparse representation method for magnetic resonance spectroscopy quantification. IEEE Transactions on Biomedical Engineering, 57 (7), 1620-1627.Search in Google Scholar

[22] Wei, Y., Qiu, J., Karimi, H.R., Wang, M. (2014). H-infinity model reduction for continuous-time Markovian jump systems with incomplete statistics of mode information. International Journal of Systems Science, 45 (7), 1496-1507.10.1080/00207721.2013.837545Search in Google Scholar

[23] Wei, Y., Qiu, J., Karimi, H.R., Wang, M. (2014). New results on H-infinity dynamic output feedback control for Markovian jump systems with time-varying delay and defective mode information. Optimal Control, Applications and Methods, 35 (6), 656-675.10.1002/oca.2093Search in Google Scholar

[24] Wei, Y., Qiu, J., Karimi, H.R., Wang, M. (2015). Quantized H-infinity filtering for continuous-time Markovian jump systems with deficient mode information. Asian Journal of Control, 17 (6), 1914-1923.10.1002/asjc.1052Search in Google Scholar

[25] Yang, J., Wright, J., Huang, T. (2010). Image Super-resolution via sparse representation. IEEE Transactions on Image Processing, 19 (11), 2861-2873.10.1109/TIP.2010.2050625Search in Google Scholar

[26] Wang, J., Zhu, S., Gong, Y. (2010). Resolution enhancement based on learning the sparse association of image paths. Pattern Recognition Letters, 31 (1), 1-10.Search in Google Scholar