本文提出多组耦合字典及其交替学习算法，实现图像超分辨率重建. 在字典学习阶段将训练图像视为高分辨率图像，将它先缩小再放大得到低分辨率图像. 两图像之差为残差图像. 从残差图像块和低分辨率图像块特征的联合数据中学习耦合字典，得到残差图像和低分辨率图像间的映射关系. 针对图像块具有不同纹理和结构以及
字典学习效率的问题，提出多组耦合字典和字典交替学习算法. 在重建阶段先将输入图像插值放大，视为低分辨率图像. 求出低分辨率图像块对于每组耦合字典中低分辨率部分的稀疏表示误差，取表示误差最小的耦合字典中残差部分重建残差图像，与低分辨率图像融合得到高分辨率图像. 实验结果表明该方法具有良好的重建效果.
A super-resolution image reconstruction method based on multi-groups of coupled dictionary and alternative learning is presented. In the dictionary learning phase, the image from a training set is viewed as high resolution (HRI). A reduced and re-enlarged version of the HRI is low resolution (LRI). The difference
between HRI and LRI is the residual. The mapping between residual and LRI is obtained from the coupled dictionaries based on the joint data composed of residual patch and LRI patch features. In the reconstruction phase, an enlarged version of the input image is taken as LRI. For each LRI patch, sparse representations and corresponding errors are calculated by using low resolution components of each group of the coupled dictionary. The residual components of coupled dictionary with minimum errors is used to reconstruct the corresponding residual image patch. All reconstructed residual patches together are used to form a residual image, which is then combined with the LRI to produce an HRI. The experimental results demonstrate a satisfied superresolution reconstruction quality.
[1]FREEMAN W, JONES T, PASZTOR E. Example-based super-resolution [J]. IEEE Computer Graphics and Applications, 2002, 22(2): 56-65.
[2]CHANG H, YEUNG Y, XIONG Y. Super-resolution through neighbor embedding [C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2004, 1:275-282.
[3]CHAN T, ZHANG J, PU J. Neighbor embedding based super-resolution algorithm through edge detection and feature selection [J]. Pattern Recognition Letters, 2009, 30(5): 494-502.
[4]GLASNER D, BAGON S, IRANI M. Super-resolution from a single image [C]//Proceedings of IEEE International Conference on Computer Vision, 2009: 349-356.
[5]YANG J, WRIGHT J, et al. Image super-resolution as sparse representation of raw image patches [C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2008: 1-8.
[6]YANG J, WRIGHT J, HUANG T, MA Y. Image super-resolution via sparse representation [J]. IEEE Transactions on Image Processing, 2010, 19(11):2861-2873.
[7]ZEYDE R, ELAD M, PROTTER M. On single image scale-up using sparse-representations [C]// Proceedings of Curves & Surfaces, Avignon-France, June 24-30, 2010.
[8]AHARON M, ELAD M, BRUCKSTEIN A. The K-SVD: An algorithm for designing of over-complete dictionaries for sparse representations [J]. IEEE Transactions on Signal Processing, 2006, 54(11):4311-4322.
[9]ENGAN K, AASE S. Method of optimal directions for frame design [C]//Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, 1999, 5:2443-2446.
[10]YANG M, WANG C, HU T, WANG Y. Learning Context-Aware sparse representation for single image super-resolution [C]//Proceedings of IEEE International Conference on Image Processing, 2011.
[11]张学工. 统计学习理论的本质[M], 北京: 清华大学出版杜, 2000.
