基于四通道卷积稀疏编码的图像超分辨率重建方法

doi:10.11772/j.issn.1001-9081.2017112742

计算机应用 ›› 2018, Vol. 38 ›› Issue (6): 1777-1783.DOI: 10.11772/j.issn.1001-9081.2017112742

• 虚拟现实与多媒体计算 • 上一篇下一篇

基于四通道卷积稀疏编码的图像超分辨率重建方法

陈晨, 赵建伟, 曹飞龙

中国计量大学信息与数学系, 杭州 310018

收稿日期:2017-11-19 修回日期:2017-12-29 出版日期:2018-06-10 发布日期:2018-06-13
通讯作者: 赵建伟
作者简介:陈晨(1992-),男,浙江杭州人,硕士研究生,主要研究方向:智能计算、图像处理;赵建伟(1977-),女,浙江金华人,教授,博士,CCF会员,主要研究方向:智能计算、图像处理;曹飞龙(1965-),男,浙江台州人,教授,博士,CCF会员,主要研究方向:智能计算、图像处理。
基金资助:
国家自然科学基金资助项目（61571410，61672477）；浙江省自然科学基金资助项目（LY18F020018）。

Image super-resolution reconstruction based on four-channel convolutional sparse coding

CHEN Chen, ZHAO Jianwei, CAO Feilong

Department of Information and Mathematics, China Jiliang University, Hangzhou Zhejiang 310018, China

Received:2017-11-19 Revised:2017-12-29 Online:2018-06-10 Published:2018-06-13
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61571410, 61672477), the Natural Science Foundation of Zhejiang Province (LY18F020018).

摘要/Abstract

摘要： 针对图像分辨率较低的问题，提出了一种基于四通道卷积稀疏编码的图像超分辨率重建方法。首先，该方法将输入图像依次翻转90°作为四通道的各自输入，通过低通滤波和梯度算子将输入图像分解成高频和低频部分；接着，分别利用卷积稀疏编码方法和三次插值方法对各通道低分辨率图像的高频部分和低频部分进行重建；最后，对四通道输出图像加权求均值获得重建的高分辨率图像。实验结果表明，所提方法比一些经典的超分辨率重建方法在峰值信噪比（PSNR）、结构相似度（SSIM）和抗噪性上具有更好的重建效果。所提方法不仅克服了重叠补丁破环图像补丁间一致性的缺陷，还提高了重建图像的细节轮廓，加强了重建图像的稳定性。

关键词: 图像重建, 超分辨率, 卷积稀疏编码, 四通道, 稳定性

Abstract: In order to solve the problem of low resolution of iamge, a new image super-resolution reconstruction method based on four-channel convolutional sparse coding was proposed. Firstly, the input image was turned over 90° in turn as the input of four channels, and an input image was decomposed into the high frequency part and the low frequency part by low pass filter and gradient operator. Then, the high frequency part and low frequency part of the low resolution image in each channel were reconstructed by convolutional sparse coding method and cubic interpolation method respectively. Finally, the four-channel output images were weighted for mean to obtain the reconstructed high resolution image. The experimental results show that the proposed method has better reconstruction effect than some classical super-resolution methods in Peak Signal-to-Noise Ratio (PSNR), Structural SIMilarity (SSIM) and noise immunity. The proposed method can not only overcome the shortcoming of consistency between image patches destroyed by overlapping patches, but also improve the detail contour of reconstructed image, and enhance the stability of reconstructed image.

Key words: image reconstruction, super-resolution, convolutional sparse coding, four-channel, stability

中图分类号:

陈晨, 赵建伟, 曹飞龙. 基于四通道卷积稀疏编码的图像超分辨率重建方法[J]. 计算机应用, 2018, 38(6): 1777-1783.

CHEN Chen, ZHAO Jianwei, CAO Feilong. Image super-resolution reconstruction based on four-channel convolutional sparse coding[J]. Journal of Computer Applications, 2018, 38(6): 1777-1783.

参考文献

[1] 秦峰,孙丰荣,宋尚玲,等.基于压缩感知的微分相衬CT迭代图像重建[J].计算机应用,2013,33(6):1732-1736.(QIN F, SUN F R, SONG S L, et al. Iterative image reconstruction for differential phase contrast CT based on compressive sensing[J]. Journal of Computer Applications, 2013, 33(6):1732-1736.)
[2] 张晓梦,杨宏成,张涛.定步长压缩感知锥束CT重建算法[J].计算机应用,2014,34(2):553-557.(ZHANG X M, YANG H C, ZHANG T. Compressing-sensing cone-beam CT reconstruction algorithm of fixed step-size[J]. Journal of Computer Applications, 2014, 34(2):553-557.)
[3] 郭晓,谭文安.基于级联深度卷积神经网络的高性能图像超分辨率重构[J].计算机应用,2017,37(11):3124-3127.(GUO X, TAN W A. High-performance image super-resolution restruction based on cascade deep convolutional network[J]. Journal of Computer Applications, 2017, 37(11):3124-3127.)
[4] TIMOFTE R, DE V, GOOL L V. Anchored neighborhood regression for fast example-based super-resolution[C]//Proceedings of the 2013 IEEE International Conference on Compututer Vision. Piscataway, NJ:IEEE, 2013:1920-1927.
[5] TIMOFTE R, SMET VD, GOOL LV. A+:adjusted anchored neighborhood regression for fast super-resolution[C]//Proceedings of the 2014 Asian Conference on Compututer Vision, LNCS 9006. Cham:Springer, 2014:111-126.
[6] YANG J C, WRIGHT J, HUANG T, et al. Image super-resolution as sparse representation of raw image patches[C]//Proceedings of the 2008 IEEE International Conference on Compututer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2008:1-8.
[7] YANG J C, WRIGHT J, HUANG T S, et al. Image super-resolution via sparse representation[J]. IEEE Transaction on Image Processing, 2010, 19(11):2861-2873.
[8] ZEYDE R, ELAD M, PROTTER M. On single image scale-up using sparse-representations[C]//Proceedings of the 20107th International Conference on Curves and Surfaces, LNCS 6920. Berlin:Springer, 2010:711-730.
[9] CANDES E J, TAO T. Decoding by linear programming[J]. IEEE Transactions on Information Theory, 2005, 51(12):4203-4215.
[10] CANDES E J. Compressive sampling[EB/OL].[2017-10-16]. http://www.icm2006.org/proceedings/Vol_Ⅲ/contents/ICM_Vol_3_69.pdf.
[11] ZEILER M D, KRISHNAN D, TAYLOR G W, et al. Deconvolutional networks[C]//Proceedings of the 2010 IEEE International Conference on Compututer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2010:2528-2535.
[12] GU S H, ZUO W M, XIE Q, et al. Convolutional sparse coding for image super-resolution[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway, NJ:IEEE, 2015:1823-1831.
[13] CHATFIELD K, SIMONYAN K, VEDALDI A, et al. Return of the devil in the details:delving deep into convolutional nets[EB/OL].[2017-10-16]. http://www.robots.ox.ac.uk/~vedaldi/assets/pubs/chatfield14return.pdf.
[14] ZHONG L W, KWOK J T. Fast stochastic alternating direction method of multipliers[EB/OL].[2017-10-16]. http://www.cse.ust.hk/faculty/jamesk/papers/icml14a.pdf.
[15] WOHLBERG B. Efficient convolutional sparse coding[C]//Proceedings of the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway, NJ:IEEE, 2014:7173-7177.
[16] HE L, QI H R, ZARETZKI R. Beta process joint dictionary learning for coupled feature spaces with application to single image super-resolution[C]//Proceedings of the 2013 IEEE International Conference on Compututer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2013:345-352.
[17] TAI Y W, LIU S C, BROWN M S, et al. Super resolution using edge prior and single image detail synthesis[C]//Proceedings of the 2010 IEEE International Conference on Compututer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2010:2400-2407.
[18] WANG Z, BOVIK A C. A universal image quality index[J]. IEEE Signal Processing Letters, 2002, 9(3):81-84.
[19] WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment:from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4):600-612.
[20] DONG W S, ZHANG L, 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.

基于四通道卷积稀疏编码的图像超分辨率重建方法

Image super-resolution reconstruction based on four-channel convolutional sparse coding

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