基于TV-L1分解的红外云图超分辨率算法

符冉迪; 周颖; 颜文; 尹曹谦

doi:10.3788/OPE.20162404.0937

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基于TV-L¹分解的红外云图超分辨率算法

信息科学 | 更新时间：2020-08-13

- 基于TV-L¹分解的红外云图超分辨率算法
- Infrared nephogram super-resolution algorithm based on TV-L¹ decomposition
- 光学精密工程 2016年24卷第4期页码：937-944
- 作者机构：
  
  宁波大学信息科学与工程学院,浙江宁波,315211
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.61471212)();浙江省信息与通信工程重中之重学科资助项目(No.XKXL1306)();浙江省教育厅科研项目(No.Y20122
- DOI：10.3788/OPE.20162404.0937
  中图分类号： TP391.4
- 收稿日期：2015-12-02，
  
  修回日期：2016-02-02，
  
  纸质出版日期：2016-04-25
- 稿件说明：
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符冉迪, 周颖, 颜文等. 基于TV-L1分解的红外云图超分辨率算法[J]. 光学精密工程, 2016,24(4): 937-944

FU Ran-di, ZHOU Ying, YAN Wen etc. Infrared nephogram super-resolution algorithm based on TV-L1 decomposition[J]. Editorial Office of Optics and Precision Engineering, 2016,24(4): 937-944
符冉迪, 周颖, 颜文等. 基于TV-L1分解的红外云图超分辨率算法[J]. 光学精密工程, 2016,24(4): 937-944 DOI： 10.3788/OPE.20162404.0937.

FU Ran-di, ZHOU Ying, YAN Wen etc. Infrared nephogram super-resolution algorithm based on TV-L1 decomposition[J]. Editorial Office of Optics and Precision Engineering, 2016,24(4): 937-944 DOI： 10.3788/OPE.20162404.0937.

摘要

提出一种基于TV-L

分解的红外云图超分辨率算法。该方法采用原始-对偶算法求解TV-L

图像分解模型

将低分辨率云图分解为结构部分和纹理部分:对结构部分采用软决策自适应插值(SAI)处理;对纹理部分则基于非下采样Contourlet变换(NSCT)具有多方向和平移不变的特性

构造非线性增益函数对其NSCT变换域系数进行处理

然后对处理后的变换系数进行NSCT逆变换实现纹理增强。最后

将处理后的结构部分和纹理部分组合起来得到重构的高分辨率云图。实验结果表明

所提出的算法在视觉效果以及图像质量定量评价上均优于传统插值方法

在实现两倍超分辨率时

其峰值信噪比(PSNR)和结构相似度(SSIM)平均值分别提高了1.3162~4.5919 dB和0.0071~0.0206;实现三倍超分辨率时PSNR和SSIM平均值分别提高了0.3387~4.58 dB和0.0018~0.0417。由于SAI插值和非下釆样Contourlet变换准确表示了云图的不同形态特征

故所提算法的超分辨率结果不但准确重建了云图中的结构部分

而且有效保持了红外云图纹理和边缘。

Abstract

A super-resolution algorithm based on TV-L

decomposition was proposed. In the algorithm

the original-dual algorithm was used to solve the TV-L

image decomposition model

and the low resolution image was decomposed into the structure and the texture parts. The structure part was processed with a soft decision adaptive interpolation. For the texture part

the Nonsubsampled Contourlet Transform (NSCT) characterized by multi-direction and shift-invariance was used to construct the nonlinear gain function to process the NSCT transform domain coefficients

then the processed transform coefficients were enhanced their textures by the NSCT inverse transform. Finally

the reconstructed high resolution image was obtained by combining the processed the structure and texture parts. Experimental results show that the proposed algorithm in both visual effect and the quantitative evaluation on image quality is better than the traditional interpolation method. For realizing twice super-resolution

its peak signal-to-noise ratio (PSNR) and Structural Similarity (SSIM) average value are increased by 1.316 2-4.591 9 dB and 0.007 1-0.020 6. For realizing three super-resolution

the PSNR and the SSIM are increased by 0.338 7-4.58 0 dB and 0.001 8-0.041 7

respectively. Because of the accurate representation of the different morphological features of the cloud image

the SAI interpolation and NSCT not only reconstruct the smooth component

but also maintain the texture and edge of the infrared nephogram.

关键词

Keywords

references

李秀馨, 王敬东, 徐烨晔. 基于改进FCM算法的卫星云图聚类方法研究[J]. 红外技术, 2013, 35(3):150-154. LI X X, WANG J D, XU Y Y. Satellite image clustering research based on improved FCM algorithm[J]. Infrared Technology, 2013, 35(3):150-154.(in Chinese)

GREENSPAN H. Super-resolution in MRI[C].Preceedings of the 2002 IEEE International Symposium on Biomedical Imaging, 2002:943-946.

KENNEDY J A, ISRAEL O, FRENKEL A, et al.. Super-resolution in PET imaging[J].IEEE Transactions on Medical Imaging, 2006, 25(2):137-147.

ZHENG Z H, WANG B, SUN K. Single remote sensing image super-resolution and denoising via sparse representation[C].2011 International Workshop on Multi-Sensor Remote Sensing and Mapping (M2RSM), 2011:1-5.

翟海天, 李辉, 李彬. 基于区域划分的红外超分辨率重建[J]. 光学精密工程, 2015, 23(10):2989-2996. ZHAI H T, LI H, LI B.Infrared super resolution reconstruction based on region division[J].Opt. Precision Eng., 2015, 23(10):2989-2996. (in Chinese)

龚卫国, 潘飞宇, 李进明. 用双层重建法实现单幅图像的超分辨率重建[J]. 光学精密工程, 2014, 22(3):720-729. GONG W G, PAN F Y, LI J M.Single-image super-resolution reconstruction via double layer reconstrcuting[J].Opt. Precision Eng., 2014, 22(3):720-729. (in Chinese)

JING G D, SHI Y H, LU B. Single-image super-resolution based on decomposition and sparse resolution[C]. 2010 International Conference on Multimedia Communications (Mediacom), 2010:127-130.

VINCENGT L G.Cartoon+Texture image decomposition by the TV-L1 model[J].Image Processing On Line, 2014, 4:204-219.

ZHANG 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.

周妍, 李庆武, 霍冠英. 基于非下采样Contourlet变换系数直方图匹配的自适应图像增强[J]. 光学精密工程, 2014, 22(8):2214-2222. ZHOU Y, LI Q W, HUO G Y. Adaptive image enhancement based on NSCT coefficient histogram matching[J].Opt. Precision Eng., 2014, 22(8):2214-2222. (in Chinese)

RUDIN L, OSHER S, FATEMI E. Nonlinear total variation based noise removel algorithms[J]. Physica D, 1992, 60(1-4):259-268.

CHAN T F, ESEDOGLU S. Aspects of total variation regularized L1 function approximation[J]. SIAM Journal on Applied Mathematics, 2005, 65(5):1817-1837.

CHAMBOLLE A, POCK T.A first-order primal-dual algorithm for convex problems with applications to imaging[J]. Journal of Mathematical Imaging and Vision, 2011, 40(1):120-145.

赵文达, 赵建, 韩希珍, 等. 基于变分偏微分方程的红外图像增强算法研究[J]. 液晶与显示, 2014, 29(2):281-285. ZHAO W D, ZHAO J, HAN X ZH. Infrared image enhancement based on variational partial differential equations[J].Chinese Journal of Liquid Crystals and Displays, 2014, 29(2):281-285. (in Chinese)

da CUNHA A L, ZHOU J P, DO M N. The nonsubsampled contourlet transform:Theory, design, and applications[J]. IEEE Transactions on Image Processing, 2006, 15(10):3089-3101.

DO M N, VETTERLI M. The Contourlet transform:an efficient directional multiresolution image representation[J]. IEEE Transactions on Signal Processing, 2005, 14(12):2091-2106.

杨桄, 童涛, 孟强强. 基于梯度加权的红外与可见光图像融合方法[J]. 红外与激光工程, 2014, 43(8):2772-2779. YANG G, TONG T, MENG Q Q. Infrared and visible images fusion method based on gradient weighted[J]. Infrared and Laser Engineering, 2014, 43(8):2772-2779.(in Chinese)

ZHOU F, MA X L, LI Y. Medical image enhancement based on NSCT[C]. International Communications Satellite Systems Conference, 2013:166-169.

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