基于两级字典与分频带字典的图像超分辨率算法

练秋生; 张钧芹; 陈书贞

doi:10.3724/SP.J.1004.2013.01310

基于两级字典与分频带字典的图像超分辨率算法

doi: 10.3724/SP.J.1004.2013.01310

1.
燕山大学信息科学与工程学院秦皇岛 066004

基金项目:

国家自然科学基金(61071200, 60772079);河北省自然科学基金(F2010001294)资助

详细信息

作者简介:
张钧芹燕山大学硕士研究生. 主要研究方向为超分辨率.E-mail: zhangjunqin1111@163.com

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- 被引次数: 0
出版历程
- 收稿日期: 2011-09-06
- 修回日期: 2012-11-07
- 刊出日期: 2013-08-20

Single Image Super-resolution Algorithm Based on Two-stage and Multi-frequency-band Dictionaries

1.
Institute of Information Science and Technology, Yanshan University, Qinhuangdao 066004

Funds:

Supported by National Natural Science Foundation of China (61071200, 60772079) and Natural Science Foundation of Hebei Province (F2010001294)

摘要

摘要: 常规基于稀疏表示的超分辨率算法使用一级高低分辨字典重构图像, 恢复细节信息不充分. 本文利用两级字典恢复尽可能多的细节信息; 然后构造联合低频字典、中频字典、高频字典的分频带字典, 利用图像低频、中频、高频三者之间的预测关系, 恢复图像中的高频信息. 利用图像的非局部相似性, 将其与迭代反向投影算法相结合, 进行图像的后处理. 实验结果表明, 与其他几种基于学习的算法相比, 本算法无论是在峰值信噪比、结构相似性指标, 还是视觉效果上都有显著的提高.
- 超分辨率 /
- 稀疏表示 /
- 字典 /
- 非局部相似性
Abstract: The conventional super-resolution algorithms on sparse representation reconstruct the high resolution image using one-stage high/low resolution dictionary pairs with inadequate detail information. In order to recover detail information as much as possible, two-stage dictionaries are explored in this paper. Then we train jointly multiple-frequency-band dictionaries consisting of low frequency (LF) dictionaries, middle frequency (MF) dictionaries and high frequency (HF) dictionaries, and simultaneously exploit the prediction relation of LF component, MF component and HF component to recover middle and high frequency information. Considering that there are many repetitive structures in the natural image, nonlocal self-similarity information is combined properly with iterative back-projection procedure to post-process the image. Experimental results demonstrate that the proposed algorithm has remarkable improvement in peak signal-to-noise ratio, structural similarity and visual quality compared with the other learning-based algorithms.
- Super-resolution /
- sparse representation /
- dictionaries /
- nonlocal self-similarity

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参考文献(25)

[1]	Milanfar P. Super-Resolution Imaging. USA: CRC Press, 2010
[2]	Park S C, Park M K, Kang M G. Super-resolution image reconstruction: a technical overview. IEEE Signal Processing Magazine, 2003, 20(3): 21-36
[3]	Li X, Orchard M T. New edge-directed interpolation. IEEE Transactions on Image Processing, 2001, 10(10): 1521-1527
[4]	Dai S Y, Han M, Wu Y, Gong Y H. Bilateral back-projection for single image super resolution. In: Proceedings of the 2007 IEEE International Conference on Multimedia and Expo. Beijing China: IEEE, 2007. 1039-1042
[5]	Sun J, Xu Z B, Shum H Y. Gradient profile prior and its applications in image super-resolution and enhancement. IEEE Transactions on Image Processing, 2011, 20(6): 1529-1542
[6]	Dong W S, Zhang L, Shi G M, Wu X L. Nonlocal back-projection for adaptive image enlargement. In: Proceedings of the 16th IEEE International Conference on Image Processing. Cairo, Egypt: IEEE, 2009. 349-352
[7]	Freeman W T, Pasztor E C, Carmichael O T. Learning low-level vision. International Journal of Computer Vision, 2000, 40(1): 25-47
[8]	Freeman W T, Jones T R, Pasztor E C. Example-based super-resolution. IEEE Computer Graphics and Applications, 2002, 22(2): 56-65
[9]	Sun J, Zheng N N, Tao H, Shum H Y. Image hallucination with primal sketch priors. In: Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Madison Wisconsin, USA: IEEE, 2003. 729-736
[10]	Chang H, Yeung D Y, Xiong Y M. Super-resolution through neighbor embedding. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington DC, USA: IEEE, 2004. 275-282
[11]	Yang J C, Wright J, Ma Y, Huang T. Image super-resolution as sparse representation of raw image patches. In: Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Anchorage, USA: IEEE, 2008. 1-8
[12]	Yang J C, Wright J, Huang T, Ma Y. Image super-resolution via sparse representation. IEEE Transactions on Image Processing, 2010, 19(11): 2861-2873
[13]	Zeyde R, Elad M, Protter M. On single image scale-up using sparse-representations. In: Proceedings of the 7th International Conference on Curves and Surfaces. Berlin, Heidelberg: Springer, 2012. 711-730
[14]	Glasner D, Bagon S, Irani M. Super-resolution from a single image. In: Proceedings of IEEE 12th International Conference on Computer Vision. Kyoto, Japan: IEEE, 2009. 349-356
[15]	Yang C Y, Huang J B, Yang M H. Exploiting self-similarities for single frame super-resolution. In: Proceedings of the 10th Asian conference on Computer vision. Berlin, Heidelberg: Springer, 2011. 497-510
[16]	Dong W S, Shi G M, Zhang L, Wu X L. Super-resolution with nonlocal regularized sparse representation. In: Proceedings of the 2010 SPIE Conference on Visual Communications and Image Processing. Huangshan, China: IEEE, 2010. 1-10
[17]	Dong W S, Zhang L, Shi G M, Wu X L. Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Transactions on Image Processing, 2011, 20(7): 1838-1857
[18]	Lee H, Battle A, Raina R, Ng A Y. Efficient sparse coding algorithms. In: Proceedings of the 2007 Neural Information Processing Systems. Vancouver, Canada: Citeseer, 2007. 801-808
[19]	Yu G S, Sapiro G, Mallat S. Image modeling and enhancement via structured sparse model selection. In: Proceedings of the 17th IEEE International Conference on Image Processing. Hong Kong, China: IEEE, 2010. 1641-1644
[20]	Aharon M, Elad M, Bruckstein A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322
[21]	Starck J L, Fadili J, Murtagh F. The undecimated wavelet decomposition and its reconstruction. IEEE Transactions on Image Processing, 2007, 16(2): 297-309
[22]	Irani M, Peleg S. Motion analysis for image enhancement: resolution, occlusion, and transparency. Journal of Visual Communication and Image Representation, 1993, 4(4): 324-335
[23]	Tibshirani R. Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society Series B, 1996, 58(1): 267-288
[24]	Efron B, Hastie T, Johnstone I, Tibshirani R. Least angle regression. The Annals of Statistics, 2004, 32(2): 407-451
[25]	Garg R, Khandekar R. Gradient descent with sparsification: an iterative algorithm for sparse recovery with restricted isometry property. In: Proceedings of the 26th International Conference on Machine Learning. New York, USA: ACM, 2009. 337-344