[1] Irani M, Peleg S. Improving resolution by image registration[J]. Cvgip Graphical Models & Image Processing, 1991, 53(3): 231-239.
[2] Lu X, Li X. Multiresolution imaging[J]. IEEE Trans Cybern, 2014, 44(1): 149-160.
[3] Hou H S, Andrews H C. Cubic spline for image interpolation and digital filtering[J]. IEEE Transaction on Signal Pressing, 1978, 26(6): 508-517.
[4] Tai Y W, Liu S, Brown M S, et al. Super resolution using edge prior and single image detail synthesis[C]//Computer Vision and Pattern Recognition. IEEE, 2010: 2400-2407.
[5] Sun J, Sun J, Xu Z, et al. Image super-resolution using gradient profile prior[C]//IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2008: 1-8.
[6] You X, Li Q, Tao D, et al. Local metric learning for exemplar-based object detection[J]. IEEE Transactions on Circuits & Systems for Video Technology, 2014, 24(8):1265-1276.
[7] Yu J, Gao X, Tao D, et al. A unified learning framework for single image super-resolution[J]. IEEE Trans Neural Netw Learn Syst, 2014, 25(4): 780-792.
[8] Ren J, Liu J, Guo Z. Context-aware sparse decomposition for image denoising and super-resolution[J]. IEEE Transactions on Image Processing, 2013, 22(4): 1454-1467.
[9] Romano Y, Protter M, Elad M. Single image interpolation via adaptive nonlocal sparsity-based modeling[J]. IEEE Transactions on Image Processing, 2014, 23(7): 3085.
[10] Yang J, Wright J, Huang T, et al. Image super-resolution as sparse representation of raw image patches[C]//Computer Vision and Pattern Recognition, 2008, IEEE Conference on. IEEE, 2008: 1-8.
[11] Wang S, Zhang D, Liang Y, et al. Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis[C]//Computer Vision and Pattern Recognition. IEEE, 2012: 2216-2223.
[12] Zeiler M D, Krishnan D, Taylor G W, et al. Deconvolutional networks[C]//Computer Vision and Pattern Recognition. IEEE, 2010: 2528-2535.
[13] Gu S, Zuo W, Xie Q, et al. Convolutional sparse coding for image super-resolution[C]//IEEE International Conference on Computer Vision. IEEE, 2015: 1823-1831.
[14] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. American Association for the Advancement of Science, 2006, 313(5786): 504-507.
[15] Hinton G E, S.Osindero Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527-1554.
[16] Lee H, Ekanadham C, Ng A Y. Sparse deep belief net model for visual area V2[C]//International Conference on Neural Information Processing Systems. Curran Associates Inc, 2007: 873-880.
[18] Lee Q V, Ngiam J, Coates A, et al. On optimization meyhods for deep learning[C]//Proceedings of the 28th International Methods for Deep Learning, 2011: 265-272.
[19] Kivinen J, Warmuth M K. Exponentiated gradient versus gradient descent for linear predictors[J]. Information and Computation, 1997, 132(1): 1-63.
[20] Wohlberg B. Efficient convolutional sparse coding[C]//IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2014: 7173-7177.
[21] Zhong L W, Kwok J T. Fast stochastic alternating direction method of multipliers[C]//International Conference on International Conference on Machine Learning, 2014: I-46.
[22] Kwang In Kim, Younghee Kwon. Single-image super-resolution using sparse regression and natural image prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(6): 1127.
