Image Super-resolution Reconstruction Based on Kernel Partial Least Squares and Weighted Boosting

Park S C, Park M K, and Kang M G. Super-resolution image reconstruction: a technical overview[J].IEEE Signal Processing Magazine.2003, 20(3):21-36[2]杨浩, 高建坡, 吴镇扬. 一种新的图像配准和超分辨率重建算法[J].电子与信息学报.2008, 30(1):168-171浏览[3]Yang Hao, Gao Jian-po, and Wu Zhen-yang. A new algorithm for image registration and super-resolution reconstruction[J].Journal of Electronics Information Technology.2008, 30(1):168-171[5]Glasner D.[J].Bagon S, and Irani M. Super-resolution from a single image[C]. IEEE 12th International Conference on Computer Vision, Kyoto, Japan.2009,:-[6]乔建苹, 刘琚, 闫华, 等. 基于Log-WT的人脸图像超分辨率重建[J].电子与信息学报.2008, 30(6):1276-1280浏览[7]Qiao Jian-ping, Liu Ju, Yan Hua, et al.. A Log-WT based super-resolution algorithm[J].Journal of Electronics Information Technology.2008, 30(6):1276-1280[8]Chan T M, Zhang J, Pu J, et al.. Neighbor embedding based super-resolution algorithm through edge detection and feature selection[J].Pattern Recognition Letters.2009, 30(5):494-502[9]Yang J, Wright J, Huang T, et al.. Image super-resolution via sparse representation[J].IEEE Transactions on Image Processing.2010, 19(11):2861-2873[10]李民, 李世华, 李小文, 等. 非局部联合稀疏近似的超分辨率重建算法[J].电子与信息学报.2011, 33(6):1407-1412浏览[11]Li Min, Li Shi-hua, Li Xiao-wen, et al.. Super-resolution reconstruction algorithm based on non-local simultaneous sparse approximation[J].Journal of Electronics Information Technology.2011, 33(6):1407-1412[12]胡宇, 赵保军, 沈庭芝, 等. 基于偏最小二乘的人脸超分辨率重构[J]. 北京理工大学学报, 2010, 30(9): 1098-1101.[14]Wu W, Liu Z, and He X. Learning-based super resolution using kernel partial least squares[J].Image and Vision Computing.2011, 29(6):394-406[15]王惠文, 吴载斌, 孟洁. 偏最小二乘回归的线性与非线性方法[M]. 北京: 国防工业出版社, 2006: 97-104, 215-225.[17]Rosipal R and Krmer N. Overview and recent advances in partial least squares[J].Lecture Notes in Computer Science.2006, 3940:34-51[18]Chen S, Wang J, Ouyang Y, et al.. Boosting part-sense multi-feature learners toward effective object detection[J].Computer Vision and Image Understanding.2011, 115(3):364-374[19]Chang C C. A boosting approach for supervised Mahalanobis distance metric learning[J].Pattern Recognition.2012, 45(2):844-862[20]Suresh S, Sundararajan N, and Saratchandran P. Risk- sensitive loss functions for sparse multi-category classification problems[J].Information Sciences.2008, 178(15):2621-2638[21]Gao X, Zhang K, Tao D, et al.. Joint learning for single image super-resolution via coupled constraint[J].IEEE Transactions on Image Processing.2012, 21(2):469-480[22]Wang Z, Bovik A C, Sheikh H R, et al.. Image quality assessment: from error measurement to structural similarity[J].IEEE Transactions on Image Processing.2004, 13(4):600-612