Single image super-resolution reconstruction using multiple dictionaries and improved iterative back-projection

Abstract

In order to improve the super-resolution reconstruction effect of the single image, a novel multiple dictionaries learning via support vector regression (SVR) and improved iterative back-projection (IBP) are proposed. To characterize the image structure, the low-frequency dictionary is constructed from the normalized brightness of low-frequency image patches in a discrete-cosine-transform (DCT) domain. Pixels determined by Gaussian weighting are added to the input vector to restore more high-frequency information when training the high-frequency image patch dictionary in the space domain. During post-processing, the improved IBP is employed to reduce regression errors each time. Experiment results show that the peak signal-to-noise ratio (PSNR)and structural similarity (SSIM) of the proposed method are enhanced by 1.6%–5.5% and 1.5%–13.1% compared with those of bicubic interpolation, and the proposed method visually outperforms several algorithms.

Keywords

A

References

[1] Chanzi L, Qingchun C, Hengchao L. Multimedia Tools and Applications. 2017, 76: 14759

[2] Qi Y, Yanzhu Z, Tiebiao Z, Yangquan C. ISA Transactions. 2017, 82: 163

[3] Xiang-jun Z, Xiao-lin W. IEEE Transactions on Image Processing. 2008, 17: 887

[4] Jafari-Khouzani K. IEEE Transactions on Medical Imaging. 2014, 33: 1969

[5] Shao-sheng D, Jin-song L, Hai-yan X, Zhi-hui D, Qin L. Optoelectronics Letters. 2014, 10: 313

[6] Yang J, Wang Z, Lin Z, Cohen S, Huang T. IEEE Transactions on Image Processing. 2012, 21: 3467

[7] Wang Zhang-yang, Yang Ying-zhen, Wang Zhao-wen, Chang Shi-yu, Han Wei, Yang Jian-chao and Thomas S. Huang, Self-Tuned Deep Super Resolution, IEEE Conference on Computer Vision and Pattern Recognition Workshops, 1 (2015).

[8] Yuan-fei H, Jie L, Xin-bo G, Li-huo H, Wen L. IEEE Transactions on Image Processing. 2018, 27: 5904

[9] Huang D-T, Huang W-Q, Huang H, Zheng L-X. Optoelectronics Letters. 2017, 13: 439

[10] Radu Timofte, Vincent De Smet and Luc Van Gool, A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution, Asian Conference on Computer Vision, 111 (2014).

[11] Hong W, Fang-fang L, Jian-wu L. Journal of Image and Graphics. 2016, 21: 986(in Chinese)

[12] Qi-ping Y, Hai-jie L, Zhi-hong C, Xiao-ping Y. Optics and Precision Engineering. 2016, 24: 2302 in Chinese)

[13] Ni K S, Nguyen T Q. IEEE Transactions on Image Processing. 2007, 16: 1596

[14] Zhi-zhou L. Dictionary Learning Based Super-Resolution Image Reconstruction, Xian University of Electronic Technology. 2011(in Chinese)

[15] Feng-lain L, Meng-yao S, Wen-na C. Optoelectronics Letters. 2017, 13: 237

[16] Chih-chung C, Chih-jen L. ACM Transactions on Intelligent Systems and Technology. 2011, 2: 27

[17] Dong C, Loy C C, He K, Tang X. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2016, 38: 295