期刊文献+

基于过完备字典的鲁棒性单幅图像超分辨率重建模型及算法 被引量:5

Robust Single Image Super-resolution Reconstruction Model and Algorithm Based on Over-complete Dictionary
下载PDF
导出
摘要 针对单幅含噪图像的超分辨率重建问题,基于图像在过完备字典下的稀疏表示建立了超分辨率重建模型.该模型中低分辨率字典采用K-SVD算法直接训练,高分辨率字典则由高分辨率图像块与低分辨率字典下的同构的表示系数进行逼近求得;近似的高分辨率图像块通过高分辨率字典乘以表示系数得到,为使重建结果对噪声具有鲁棒性,利用基于稀疏表示的噪声图像恢复的方法由重叠的近似高分辨率图像块求得最终结果.实验结果表明,文中模型无论是主观视觉还是客观评价指标均取得了较好的效果,并验证了模型及算法的有效性. Aiming at the problem of super-resolution reconstruction for single noised image, in terms of sparse representation of over-complete dictionary, a super-resolution model is proposed. The K-SVD algorithm is used directly for learning the dictionary for low-resolution images. The dictionary for high-resolution images is got by optimizing the approximating error of the isomorphic sparse representation coefficients, which are got by learning the dictionary for low-resolution images. The representation coefficients are multiplied by the high-resolution dictionary to get the approximative high-resolution image patches. To make the reconstructed image robust to noise, the denoising method via sparse representation is used to get the final image from the overlapped approximative high- resolution image patches. The experimental results show that the proposed model obtains better outcome both in subjective visual effect and objective evaluation criteria, and demonstrates the effective of the model and algorithm.
出处 《计算机辅助设计与图形学学报》 EI CSCD 北大核心 2012年第12期1599-1605,共7页 Journal of Computer-Aided Design & Computer Graphics
基金 安徽省自然科学基金(1208085QF115)
关键词 超分辨率 单幅图像 稀疏表示 过完备字典 K—SVD super-resolution single image sparse representation over-complete dictionary K-SVD
  • 相关文献

参考文献21

  • 1Park S C, Park M K, Kang M G. Super-resolution imagereconstruction: a technical overview [J]. IEEE SignalProcessing Magazine,2003,20(3) : 21-36.
  • 2Tian J, Ma K K. A survey on super-resolution imaging [J].Signal, Image and Video Processing,2011,5(3) : 329-342.
  • 3Tsai R Y,Huang T S. Multiframe image restoration andregistration [J]. Advances in Computer Vision and ImageProcessing, 1984, (1): 317-339.
  • 4Rajan D, Chaudhuri S. Generalized interpolation and itsapplication in super-resolution imaging [J]. Image and VisionComputing,2001,19(13) : 957-969.
  • 5Lertrattanapanich S,Bose N K. High resolution imageformation from low resolution frames using Delaunaytriangulation [J]. IEEE Transactions on Image Processing,2002, 11(12): 1427-1441.
  • 6Irani M, Peleg S. Improving resolution by image registration[J]. CVGIP: Graphical Models and Image Processing, 1991,53(3): 231-239.
  • 7Stark H,Oskoui P. High-resolution image recovery fromimage-plane arrays, using convex projections [J]. Journal ofthe Optical Society of America A, 1989, 6(11) ; 1715-1726.
  • 8Freeman W T, Jones T R, Pasztor E C. Example-basedsuper-resolution [J]. IEEE Computer Graphics andApplications, 2002, 22(2) ; 56-65.
  • 9Chang H,Yeung D Y,Xiong Y M. Super-resolution throughneighbor embedding [C] //Proceedings of IEEE Conference onComputer Vision and Pattern Recognition. Los Alamitos:IEEE Computer Society Press,2004,1: 275-282.
  • 10Yang J C, Wright J,Huang T S, et al. Image super-resolution as sparse representation of raw image patches [C] //Proceedings of IEEE Conference on Computer Vision andPattern Recognition. Los Alamitos : IEEE Computer SocietyPress, 2008: 1-8.

二级参考文献53

  • 1郭强,许健民,陈桂林.三轴稳定平台下提高线列探测器光学遥感仪图像空间分辨率的方法研究[J].红外与毫米波学报,2005,24(1):39-44. 被引量:6
  • 2Tipping M E,Bishop C M.Bayesian Image Super-Resolution//Becker S,Thrun S,Obermayer K,eds.Advances in Neural Information Processing Systems.Cambridge,USA:MIT Press,2003,XVI:1279-1286.
  • 3Capel D P.Image Mosaicing and Super-Resolution.Cambridge,UK:University of Oxford,2001.
  • 4Farsiu S,Robinson M D,Elad M,et al.Fast and Robust Multiframe Super-Resolution.IEEE Trans on Image Processing,2004,13(10):1327-1344.
  • 5Freeman W T,Pasztor E C,Carmichael O T.Learning Low-Level Vision.International Journal of Computer Vision,2000,40 (1):25 -47.
  • 6Freeman W T,Jones T R,Pasztor E C.Example-Based Super-Resolution.IEEE Computer Graphics and Applications,2002,22 (2):56-65.
  • 7Liu Ce,Shum H Y,Zhang Changshui.Two-Step Approach to Hallucinating Faces:Global Parametric Model and Local Nonparametric Model// Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Hawaii,USA,2001,Ⅰ:192-198.
  • 8Chang Hong,Yeung D Y,Xiong Yimin.Super-Resolution through Neighbor Embedding//Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington,USA,2004,Ⅰ:275-282.
  • 9Fan Wei,Yeung D Y.Image Hallucination Using Neighbor Embedding over Visual Primitive Manifolds//Proc of the IEEE ComputerSociety Conference on Computer Vision and Pattern Recognition.Minneapolis,USA,2007:18-23.
  • 10Chan T M,Zhang Junping,Pu Jian,et al.Neighbor Embedding Based Super-Resolution Algorithm through Edge Detection and Feature Selection.Pattern Recognition Letters,2009,30(5):494 -502.

共引文献73

同被引文献43

  • 1Zhang X J, Wu X L. Image interpolation by adaptive 2-D autoregressive modeling and soft-decision estimation[J]. IEEE Transactions on Image Processing, 2008, 17(6): 887-896. [DOI: 10.1109/TIP.2008.924279].
  • 2Chavez R H, Ponomaryov V. Super resolution image generation using wavelet domain interpolation with edge extraction via a sparse representation[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(10): 1777-1781. [DOI: 10.1109/LGRS.2014.2308905].
  • 3Tai Y W, Liu S C, Brown M, et al. Super resolution using edge prior and single image detail synthesis[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA: IEEE, 2010: 2400-2407. [DOI: 10.1109/CVPR.2010.5539933].
  • 4Zhang K B, Gao X B, Tao D C, 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. [DOI: 10.1109/TIP.2012.2208977].
  • 5Zeyde R, Elad M, Protter M. On Single Image Scale-up Using Sparse-Representations[M]. Berlin: Springer, 2012: 711-730. [DOI: 10.1007/978-3-642-27413-8_47].
  • 6Dong W S, Zhang D, Shi G M, et al. Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization[J]. IEEE Transactions on Image Processing, 2011, 20(7): 1838-1857. [DOI: 10.1109/TIP.2011.2108306].
  • 7Nazzal M, Ozkaramanli H. Wavelet domain dictionary learning-based single image super-resolution[J]. Signal, Image and Video Processing, 2014: 1-11. [DOI: 10.1007/s11760-013-0602-7].
  • 8Xu J, Chang Z G, Fan J L. Image super-resolution by mid-frequency sparse representation and total variation regularization[J]. Journal of Electronic Imaging, 2015, 24(1): 013039-013039. [DOI: 10.1117/1.JEI.24.1.013039].
  • 9Zhou F, Liao Q M. Single-frame image super-resolution inspired by perceptual criteria[J]. IET Image Processing, 2014, 9(1): 1-11. [DOI: 10.1049/iet-ipr.2013.0808].
  • 10Zhang K B, Tao D C, Gao X B, et al. Learning multiple linear mappings for efficient single image super-resolution[J]. IEEE Transactions on Image Processing, 2015, 24(3): 846-861. [DOI: 10.1109/TIP.2015.2389629].

引证文献5

二级引证文献11

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部