Medical Image Super-Resolution Reconstruction Method Based on Non-local Autoregressive Learning
XU Jun1,2, LIU Hui1,2, YIN Yilong1,3
1.School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014 2.Digital Media Technology Key Laboratory of Shandong Province, Shandong University of Finance and Economics, Jinan 250014 3.School of Computer Science and Technology, Shandong University, Jinan 250010
Abstract：In the process of medical imaging, the image resolution is limited by radiation dose constraints and imaging equipment conditions. The accuracy of late clinical diagnosis and treatment is affected by the low resolution of the medical image. To solve this problem, a medical image super-resolution reconstruction method based on non-local autoregressive learning is proposed. According to the non-local similarity characteristic inherent in medical images, the autoregressive model based on sparse representation is applied to the super-resolution reconstruction process. Furthermore, to improve the efficiency of the experiment, the clustering algorithm is utilized to acquire the classification dictionary. The experimental results demonstrate the feasibility of the proposed method in improving the resolution of medical images as well as the reconstruction efficiency and performance.
[1] 石 敏, 白 洋, 易清明. 基于自适应多字典对的超分辨率复原算法.计算机科学与探索, 2015, 9(5): 604-610. (SHI M, BAI Y, YI Q M. Super-Resolution Restoration Algorithm Based on Adaptive Multiple Dictionary Pair. Journal of Frontiers of Computer Science and Technology, 2015, 9(5): 604-610.) [2] GIACHETTI A, ASUNI N. Real-Time Artifact-Free Image Upsca-ling. IEEE Transactions on Image Processing, 2011, 20(10): 2760-2768. [3] TSAI R Y, HUANG T S. Multiframe Image Restoration and Regi-stration. Advances in Computer Vision and Image Processing, 1984, 1(7): 317-339. [4] 郭伟伟,章品正.基于迭代反投影的超分辨率图像重建.计算机科学与探索, 2009, 3(3): 321-329. (GUO W W, ZHANG P Z. Super-Resolution Image Reconstruction with Iterative Back Projection Algorithm. Journal of Frontiers of Computer Science and Technology, 2009, 3(3): 321-329.) [5] YANG J C, WRIGHT J, HUANG T, et al. Image Super-Resolution via Sparse Representation. IEEE Transactions on Image Processing, 2010, 19(11): 2861-2873. [6] SHI F, CHENG J, WANG L, et al. LRTV: MR Image Super-Resolution with Low-Rank and Total Variation Regularizations. IEEE Transactions on Medical Imaging, 2015, 34(12): 2459-2466. [7] YANG C Y, YANG M H. Fast Direct Super-Resolution by Simple Functions // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2013: 561- 568. [8] YEGANLI F, NAZZAL M, UNAL M, et al. Image Super-Resolution via Sparse Representation over Multiple Learned Dictionaries Based on Edge Sharpness. Signal, Image and Video Processing, 2016, 10(3): 535-542. [9] DONG W S, ZHANG L, LUKAC R, et al. Sparse Representation Based Image Interpolation with Nonlocal Autoregressive Modeling. IEEE Transactions on Image Processing, 2013, 22(4): 1382-1394. [10] 潘宗序,禹 晶,胡少兴,等.基于多尺度结构自相似性的单幅图像超分辨率算法.自动化学报, 2012, 40(4): 594-603. (PAN Z X, YU J, HU S X, et al. Single Image Super Resolution Based on Multi-scale Structure Self-similarity. Acta Automatica Sinica, 2012, 40(4): 594-603.) [11] 蔡 斌,刘 卫,郑 重,等.一种改进的非局部均值去噪算法.模式识别与人工智能, 2016, 29(1): 1-10. (CAI B, LIU W, ZHENG Z, et al. An Improved Non-local Means Denoising Algorithm. Pattern Recognition and Artificial Intelligence, 2016, 29(1): 1-10.) [12] CHEN H G, HE X H, QING L B, et al. Single Image Super-Re-solution via Adaptive Transform-Based Nonlocal Self-similarity Mo-deling and Learning-Based Gradient Regularization. IEEE Transactions on Multimedia, 2017, 19(8): 1702-1717. [13] REN C, HE X H, TENG Q Z, et al. Single Image Super-Resolution Using Local Geometric Duality and Non-Local Similarity. IEEE Transactions on Image Processing, 2016, 25(5): 2168-2183. [14] CHARTRAND R, YIN W T. Iteratively Reweighted Algorithms for Compressive Sensing // Proc of the IEEE International Conference on Acoustics, Speech and Signal Processing. Washington, USA: IEEE, 2008: 3869-3872.
2017, Vol. 30 
