基于几何字典学习和耦合约束的超分辨率重建
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摘要
传统的基于稀疏表示的超分辨率重建算法对所有图像块,应用单一冗余字典表示而不能反映不同几何结构类型图像块间的区别。针对这一问题,本文探索图像局部几何结构特性,提出一种基于结构特性聚类的几何字典学习和耦合约束的超分辨率重建方法。该方法首先对训练样本图像块进行几何特性聚类,然后应用K-SVD算法为每个聚类块联合训练得到高低分辨率字典。此外,在重建过程中引入局部可控核回归和非局部相似性耦合约束,以提高重建图像质量。实验结果表明,与单一字典超分辨率算法相比,本文方法重建图像边缘和细节部分明显改善,评价参数较大提高。
Traditional super-resolution algorithms based on sparse representation of image patches exploit single redundant dictionary to represent the image patches that contain various textures, which can not reflect the differences of various image patches types. In order to overcome this disadvantage, this paper proposes a single image super resolution reconstruction method based on geometric dictionary learning and coupled regularization, by exploring the local geometric property of image patches. A large number of training image patches are clustered into several groups by their geometric property, from which the corresponding "geometric dictionaries" are learned via K-SVD algorithm which is combined with the idea that the high and low resolution dictionaries can be co-trained. In addition, a coupled regularization of local steering kernel regression and non-local similarity is introduced into the proposed method to further improve the quality of the reconstructed images. Experiment results show that the proposed method both increases the evaluation parameters and improves the visual quality of the edges and the details significantly.
Traditional super-resolution algorithms based on sparse representation of image patches exploit single redundant dictionary to represent the image patches that contain various textures, which can not reflect the differences of various image patches types. In order to overcome this disadvantage, this paper proposes a single image super resolution reconstruction method based on geometric dictionary learning and coupled regularization, by exploring the local geometric property of image patches. A large number of training image patches are clustered into several groups by their geometric property, from which the corresponding "geometric dictionaries" are learned via K-SVD algorithm which is combined with the idea that the high and low resolution dictionaries can be co-trained. In addition, a coupled regularization of local steering kernel regression and non-local similarity is introduced into the proposed method to further improve the quality of the reconstructed images. Experiment results show that the proposed method both increases the evaluation parameters and improves the visual quality of the edges and the details significantly.
引文
[1]江静,张雪松.图像超分辨率重建算法综述[J].红外技术,2012,34(1):24-30.
[2]Zhang D,Wu X.An edge-guided image interpolation algorithm via directional filtering and data fusion[J].IEEE Transactions on Image Processing,2006,15(8):2226-2238.
[3]Rasti P,Demirel H,Anbarjafari G.Image resolution enhancement by using interpolation followed by iterative back projection[C]//21st.IEEE on Signal Processing and Communications Applications Conference(SIU),2013:1-4.
[4]Yang Jian-chao,Wright J,Huang T S,et al.Image superresolution via sparse representation[J].IEEE Transactions on Image Processing,2010,19(11):2861-2873.
[5]Yang J,Wright J,Huang T,et al.Image super-resolution as sparse representation of raw image patches[C]//IEEE Conference on Computer Vision and Pattern Recognition,2008:1-8.
[6]Yu J,Gao X,Tao D,et al.A unified learning framework for single image super-resolution[J].IEEE Transactions on Neural Networks and Learning Systems,2014,25(4):780-792.
[7]Zhou F,Yuan T,Yang W,et al.Single-image super-resolution based on compact KPCA coding and Kernel regression[J].Signal Processing Letters,IEEE,2015,22(3):336-340.
[8]Peleg T,Elad M.A statistical prediction model based on sparse representations for single image super-resolution[J].IEEE Transactions on Image Processing:a Publication of the IEEE Signal Processing Society,2014,23(6):2569-2582.
[9]Zeyde R,Elad M,Protter M.On single image scale-up using sparse-representations[M].Curves and Surfaces,Springer Berlin Heidelberg,2012:711-730.
[10]Wang S,Zhang D,Liang Y,et al.Semi-coupled dictionary learning with applications to image super-resolution and photosketch synthesis[C]//Computer Vision and Pattern Recognition(CVPR),2012:2216-2223.
[11]Takeda H,Farsiu S,Milanfar P.Kernel regression for image processing and reconstruction[J].IEEE Transactions on Image Processing,2007,16(2):349-366.
[12]ZHANG Kaibing,GAO Xinbo,TAO Dacheng,et al.Single image super-resolution with non-local means and steering Kernel regression[J].IEEE Transactions on Image Processing,2012,21(11):4544-4556.
[13]首照宇,吴广祥,陈利霞.基于字典学习和非局部相似的超分辨率重建[J].计算机应用,2014,34(11):3300-3303.
[14]Rubinstein R,Peleg T,Elad M.Analysis K-SVD:A dictionarylearning algorithm for the analysis sparse model[J].IEEE Transactions on Signal Processing,2013,61(3):661-677.
[15]Feng X G,Milanfar P.Multiscale principal components analysis for image local orientation estimation[C]//IEEE Conference Record of the Thirty-Sixth Asilomar Conference on Signals,Systems and Computers,2002,1:478-482.
[16]Yang S,Wang M,Chen Y,et al.Single-image super-resolution reconstruction via learned geometric dictionaries and clustered sparse coding[J].IEEE Transactions on Image Processing,2012,21(9):4016-402.
[2]Zhang D,Wu X.An edge-guided image interpolation algorithm via directional filtering and data fusion[J].IEEE Transactions on Image Processing,2006,15(8):2226-2238.
[3]Rasti P,Demirel H,Anbarjafari G.Image resolution enhancement by using interpolation followed by iterative back projection[C]//21st.IEEE on Signal Processing and Communications Applications Conference(SIU),2013:1-4.
[4]Yang Jian-chao,Wright J,Huang T S,et al.Image superresolution via sparse representation[J].IEEE Transactions on Image Processing,2010,19(11):2861-2873.
[5]Yang J,Wright J,Huang T,et al.Image super-resolution as sparse representation of raw image patches[C]//IEEE Conference on Computer Vision and Pattern Recognition,2008:1-8.
[6]Yu J,Gao X,Tao D,et al.A unified learning framework for single image super-resolution[J].IEEE Transactions on Neural Networks and Learning Systems,2014,25(4):780-792.
[7]Zhou F,Yuan T,Yang W,et al.Single-image super-resolution based on compact KPCA coding and Kernel regression[J].Signal Processing Letters,IEEE,2015,22(3):336-340.
[8]Peleg T,Elad M.A statistical prediction model based on sparse representations for single image super-resolution[J].IEEE Transactions on Image Processing:a Publication of the IEEE Signal Processing Society,2014,23(6):2569-2582.
[9]Zeyde R,Elad M,Protter M.On single image scale-up using sparse-representations[M].Curves and Surfaces,Springer Berlin Heidelberg,2012:711-730.
[10]Wang S,Zhang D,Liang Y,et al.Semi-coupled dictionary learning with applications to image super-resolution and photosketch synthesis[C]//Computer Vision and Pattern Recognition(CVPR),2012:2216-2223.
[11]Takeda H,Farsiu S,Milanfar P.Kernel regression for image processing and reconstruction[J].IEEE Transactions on Image Processing,2007,16(2):349-366.
[12]ZHANG Kaibing,GAO Xinbo,TAO Dacheng,et al.Single image super-resolution with non-local means and steering Kernel regression[J].IEEE Transactions on Image Processing,2012,21(11):4544-4556.
[13]首照宇,吴广祥,陈利霞.基于字典学习和非局部相似的超分辨率重建[J].计算机应用,2014,34(11):3300-3303.
[14]Rubinstein R,Peleg T,Elad M.Analysis K-SVD:A dictionarylearning algorithm for the analysis sparse model[J].IEEE Transactions on Signal Processing,2013,61(3):661-677.
[15]Feng X G,Milanfar P.Multiscale principal components analysis for image local orientation estimation[C]//IEEE Conference Record of the Thirty-Sixth Asilomar Conference on Signals,Systems and Computers,2002,1:478-482.
[16]Yang S,Wang M,Chen Y,et al.Single-image super-resolution reconstruction via learned geometric dictionaries and clustered sparse coding[J].IEEE Transactions on Image Processing,2012,21(9):4016-402.