近似稀疏正则化的红外图像超分辨率重建

邓承志; 田伟; 汪胜前; 朱华生; 吴朝明; 熊志文; 钟威

doi:10.3788/OPE.20142206.1648

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近似稀疏正则化的红外图像超分辨率重建

信息科学 | 更新时间：2020-08-13

- 近似稀疏正则化的红外图像超分辨率重建
- Super-resolution reconstruction of approximate sparsity regularized infrared images
- 光学精密工程 2014年22卷第6期页码：1648-1654
- 作者机构：
  
  南昌工程学院信息工程学院,江西南昌,330099
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目（No.61162022，61362036）();江西省自然科学基金资助项目（No.20132BAB201021）();江西省科技落地计划资助项
- DOI：10.3788/OPE.20142206.1648
  中图分类号： TP391.41
- 收稿日期：2013-10-16，
  
  修回日期：2013-11-19，
  
  纸质出版日期：2014-06-25
- 稿件说明：
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邓承志, 田伟, 汪胜前等. 近似稀疏正则化的红外图像超分辨率重建[J]. 光学精密工程, 2014,22(6): 1648-1654

DENG Cheng-zhi, TIAN Wei, WANG Sheng-qian etc. Super-resolution reconstruction of approximate sparsity regularized infrared images[J]. Editorial Office of Optics and Precision Engineering, 2014,22(6): 1648-1654
邓承志, 田伟, 汪胜前等. 近似稀疏正则化的红外图像超分辨率重建[J]. 光学精密工程, 2014,22(6): 1648-1654 DOI： 10.3788/OPE.20142206.1648.

DENG Cheng-zhi, TIAN Wei, WANG Sheng-qian etc. Super-resolution reconstruction of approximate sparsity regularized infrared images[J]. Editorial Office of Optics and Precision Engineering, 2014,22(6): 1648-1654 DOI： 10.3788/OPE.20142206.1648.

摘要

针对红外图像分辨率低、受噪声影响严重等问题，引入近似稀疏正则化和K-奇异值分解（K-SVD）法，提出了基于近似稀疏表示模型的红外图像超分辨率重建方法。考虑到红外图像受到噪声污染，首先建立了稳健近似稀疏表示模型。针对已有字典训练方法时间消耗巨大问题，在假定低分辨率图像空间和高分辨率图像空间具有相似流形的前提下，联合近似稀疏表示模型和K-SVD方法，提出近似稀疏约束的基于K-SVD的高低分辨率字典对学习算法。最后，通过高分辨字典和对应的红外图像群稀疏表示系数重建得到高分辨率的红外图像。为了验证算法的性能，对提出的算法与稀疏性正则化的图像超分辨模型（SRSR）和Zeyde算法进行了实验比较。结果表明，本文方法能够较好地减少红外图像中的噪声，同时获得更好的超分辨率重建效果。

Abstract

For the problems of low-resolution and serious effect from noises of infrared images

an approximate sparsity regularized infrared image super-resolution reconstruction algorithm (ASSR) based on K-SVD (Singular Value Decomposition) was proposed. In consideration of the noise effect from infrared images

an approximate sparsity representation model was first established. On the assumption that the low and high resolution image spaces hold a similar manifold

an approximate sparsity regularized K-SVD based dictionary learning method was proposed with approximate sparsity model and K-SVD method to solve the time-consuming problem of existing dictionary training process. Finally

the high-resolution infrared images were recovered by the high-resolution dictionary and the corresponding low-resolution group sparse coefficients. To verify the performance of the algorithm proposed

it was compared with those of the Sparsity Regularized Super-Resolution Reconstruction (SRSR) and Zeyde algorithm. Experimental results show that the proposed method can reduce the noises of infrared images

and can obtain excellent performance in super-resolution reconstruction.

关键词

Keywords

references

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