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Study on infrared image super-resolution reconstruction based on an improved POCS algorithm

Shaosheng Dai, Junjie Cui, Dezhou Zhang, Qin Liu and Xiaoxiao Zhang

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 Corresponding author: Junjie Cui, Email: cuijunj@163.com

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Abstract: Aiming at the disadvantages of the traditional projection onto convex sets of blurry edges and lack of image details, this paper proposes an improved projection onto convex sets (POCS) method to enhance the quality of image super-resolution reconstruction (SRR). In traditional POCS method, bilinear interpolation easily blurs the image. In order to improve the initial estimation of high-resolution image (HRI) during reconstruction of POCS algorithm, the initial estimation of HRI is obtained through iterative curvature-based interpolation (ICBI) instead of bilinear interpolation. Compared with the traditional POCS algorithm, the experimental results in subjective evaluation and objective evaluation demonstrate the effectiveness of the proposed method. The visual effect is improved significantly and image detail information is preserved better.

Key words: POCSinfrared imagesuper-resolutioninitial estimation



[1]
Eric K, Russell C H. High resolution infrared image reconstruction using multiple, low resolution, aliased frames. Aerospace & Electronics Conference, 1996, 2(2): 702 http://proceedings.spiedigitallibrary.org/pdfaccess.ashx?url=/data/conferences/spiep/59570/142_1.pdf
[2]
Park S C, Park M K, Kang M G. Super-resolution image reconstruction: a technical overview. IEEE Signal Process Mag, 2003, 20(3): 21 doi: 10.1109/MSP.2003.1203207
[3]
Yang X, Zhang Y, Zhou D K, et al. An improved iterative back projection algorithm based on ringing artifacts suppression. Neurocomputing, 2015, 162(C): 171 https://www.researchgate.net/publication/275234318_An_improved_iterative_back_projection_algorithm_based_on_ringing_artifacts_suppression
[4]
Zhao S R, Liang H, Sarem M. A generalized detail-preserving super-resolution method. Signal Process, 2016, 120: 156 doi: 10.1016/j.sigpro.2015.09.006
[5]
Li L, Xie Y, Hu W R, et al. Single image super-resolution using combined total variation regularization by split Bregman iteration. Neurocomputing, 2014, 142(1): 551 https://www.researchgate.net/publication/273534872_Single_image_super-resolution_using_combined_total_variation_regularization_by_split_Bregman_Iteration
[6]
Stark H, Oskoui P. High resolution image recovery from image-plane arrays, using convex projections. J Opt Soc Am, 1989, 6(11): 1715 doi: 10.1364/JOSAA.6.001715
[7]
Patti A J, Sezan M I, Tekalp A M. High-resolution image reconstruction from a low-resolution image sequence in the presence of time-varying motion blur. Proceedings of ICIP-94, IEEE International Conference, 1994: 343 http://citeseerx.ist.psu.edu/showciting?cid=41039
[8]
Wei B G, Hui W H. POCS-embedded MAP method for image super-resolution restoration. IEEE Conference on Industrial Electronics & Applications, 2009: 3791
[9]
Xie W, Zhang F Y, Chen H, et al. Blind super-resolution image reconstruction based on POCS mode. Proceedings of 2009 International Conference on Measuring Technology and Mechatronics Automation, China: Changsha, 2009: 437 doi: 10.1109/ICMTMA.2009.150
[10]
Liu W R, Li S T. Sparse representation with morphologic regularizations for single image super-resolution. Signal Process, 2014, 98(5): 410 https://www.researchgate.net/publication/259509960_Sparse_representation_with_morphologic_regularizations_for_single_image_super-resolution
[11]
Yue L W, Shen H F, Yuan Q Q, et al. A locally adaptive L1-L2 norm for multi-frame super-resolution of images with mixed noise and outliers. Signal Process, 2014, 105(12): 156 https://www.researchgate.net/publication/263664193_A_locally_adaptive_L1-L2_norm_for_multi-frame_super-resolution_of_images_with_mixed_noise_and_outliers
[12]
Youla D C, Webb H. Image restoration by the method of convex projections: Part 1 Theory. IEEE Trans Med Imaging, 1982: 81 https://www.researchgate.net/publication/3222241_Image_Restoration_by_the_Method_of_Convex_Projections_Part_1Theory
[13]
Zhang F, Zhu Q D. Super-resolution image reconstruction for omni-vision based on POCS. Proceedings of Control and Decision Conference, 2009: 5045
[14]
Li J, Wu J, Hu G, et al. Super-resolution image reconstruction based on improved POCS algorithm. Springer London, 2013, 208: 743 doi: 10.1007/978-1-4471-4796-1_95
[15]
Liu C Y, Ni L. Image reconstruction based on improved POCS algorithm. Comput Eng, 2012, 38(21): 226 http://www.ecice06.com/EN/abstract/abstract24216.shtml
[16]
Zhang Y, Zhou Q. An improved method for POCS superresolution image reconstruction. International Conference on Electronics, 2011: 4150 http://ieeexplore.ieee.org/document/6066585/
[17]
Andrea G, Nicola A. Real-time artifact-free image upscaling. IEEE Trans Image Process, 2011, 20(10): 2660 https://www.researchgate.net/publication/272986914_Real_Time_Artifact-Free_Image_Upscaling
[18]
Andrea G, Nicola A. Corrections to "real-time artifact free image upscaling" [Oct 11 2760-2768]. IEEE Trans Image Process, 2012, 21(4): 2361 doi: 10.1109/TIP.2012.2186739
[19]
Gao X B, Lu W, Tao D C, et al. Image quality assessment based on multiscale geometric analysis. IEEE Trans Image Process, 2009, 18(7): 1409 doi: 10.1109/TIP.2009.2018014
[20]
Villena S, Vega M, Babacan S D, et al. Bayesian combination of sparse and non-sparse priors in image super resolution. Digital Signal Process, 2013, 23(2): 530 doi: 10.1016/j.dsp.2012.10.002
Fig. 1.  The flow chart of ICBI.

Fig. 2.  (a) LRIs. (b) Original HRI. (c) Traditional POCS method. (d) Improved POCS method. (e) Image detail enlarged from original HRI. (f) Same detail enlarged with traditional POCS method. (g) Same detail enlarged with improved POCS method.

Fig. 3.  (a) LRIs. (b) Original HRI. (c) Traditional POCS method. (d) Improved POCS method. (e) Image detail enlarged from original HRI. (f) Same detail enlarged with traditional POCS method. (g) Same detail enlarged with improved POCS method.

Fig. 4.  (a) LRIs. (b) Original HRI. (c) Traditional POCS method. (d) Improved POCS method. (e) Image detail enlarged from original HRI. (f) Same detail enlarged with traditional POCS method. (g) Same detail enlarged with improved POCS method.

Table 1.   Comparison results of Fig. 2.

Table 2.   Comparison results of Fig. 3.

Table 3.   Comparison results of Fig. 4.

[1]
Eric K, Russell C H. High resolution infrared image reconstruction using multiple, low resolution, aliased frames. Aerospace & Electronics Conference, 1996, 2(2): 702 http://proceedings.spiedigitallibrary.org/pdfaccess.ashx?url=/data/conferences/spiep/59570/142_1.pdf
[2]
Park S C, Park M K, Kang M G. Super-resolution image reconstruction: a technical overview. IEEE Signal Process Mag, 2003, 20(3): 21 doi: 10.1109/MSP.2003.1203207
[3]
Yang X, Zhang Y, Zhou D K, et al. An improved iterative back projection algorithm based on ringing artifacts suppression. Neurocomputing, 2015, 162(C): 171 https://www.researchgate.net/publication/275234318_An_improved_iterative_back_projection_algorithm_based_on_ringing_artifacts_suppression
[4]
Zhao S R, Liang H, Sarem M. A generalized detail-preserving super-resolution method. Signal Process, 2016, 120: 156 doi: 10.1016/j.sigpro.2015.09.006
[5]
Li L, Xie Y, Hu W R, et al. Single image super-resolution using combined total variation regularization by split Bregman iteration. Neurocomputing, 2014, 142(1): 551 https://www.researchgate.net/publication/273534872_Single_image_super-resolution_using_combined_total_variation_regularization_by_split_Bregman_Iteration
[6]
Stark H, Oskoui P. High resolution image recovery from image-plane arrays, using convex projections. J Opt Soc Am, 1989, 6(11): 1715 doi: 10.1364/JOSAA.6.001715
[7]
Patti A J, Sezan M I, Tekalp A M. High-resolution image reconstruction from a low-resolution image sequence in the presence of time-varying motion blur. Proceedings of ICIP-94, IEEE International Conference, 1994: 343 http://citeseerx.ist.psu.edu/showciting?cid=41039
[8]
Wei B G, Hui W H. POCS-embedded MAP method for image super-resolution restoration. IEEE Conference on Industrial Electronics & Applications, 2009: 3791
[9]
Xie W, Zhang F Y, Chen H, et al. Blind super-resolution image reconstruction based on POCS mode. Proceedings of 2009 International Conference on Measuring Technology and Mechatronics Automation, China: Changsha, 2009: 437 doi: 10.1109/ICMTMA.2009.150
[10]
Liu W R, Li S T. Sparse representation with morphologic regularizations for single image super-resolution. Signal Process, 2014, 98(5): 410 https://www.researchgate.net/publication/259509960_Sparse_representation_with_morphologic_regularizations_for_single_image_super-resolution
[11]
Yue L W, Shen H F, Yuan Q Q, et al. A locally adaptive L1-L2 norm for multi-frame super-resolution of images with mixed noise and outliers. Signal Process, 2014, 105(12): 156 https://www.researchgate.net/publication/263664193_A_locally_adaptive_L1-L2_norm_for_multi-frame_super-resolution_of_images_with_mixed_noise_and_outliers
[12]
Youla D C, Webb H. Image restoration by the method of convex projections: Part 1 Theory. IEEE Trans Med Imaging, 1982: 81 https://www.researchgate.net/publication/3222241_Image_Restoration_by_the_Method_of_Convex_Projections_Part_1Theory
[13]
Zhang F, Zhu Q D. Super-resolution image reconstruction for omni-vision based on POCS. Proceedings of Control and Decision Conference, 2009: 5045
[14]
Li J, Wu J, Hu G, et al. Super-resolution image reconstruction based on improved POCS algorithm. Springer London, 2013, 208: 743 doi: 10.1007/978-1-4471-4796-1_95
[15]
Liu C Y, Ni L. Image reconstruction based on improved POCS algorithm. Comput Eng, 2012, 38(21): 226 http://www.ecice06.com/EN/abstract/abstract24216.shtml
[16]
Zhang Y, Zhou Q. An improved method for POCS superresolution image reconstruction. International Conference on Electronics, 2011: 4150 http://ieeexplore.ieee.org/document/6066585/
[17]
Andrea G, Nicola A. Real-time artifact-free image upscaling. IEEE Trans Image Process, 2011, 20(10): 2660 https://www.researchgate.net/publication/272986914_Real_Time_Artifact-Free_Image_Upscaling
[18]
Andrea G, Nicola A. Corrections to "real-time artifact free image upscaling" [Oct 11 2760-2768]. IEEE Trans Image Process, 2012, 21(4): 2361 doi: 10.1109/TIP.2012.2186739
[19]
Gao X B, Lu W, Tao D C, et al. Image quality assessment based on multiscale geometric analysis. IEEE Trans Image Process, 2009, 18(7): 1409 doi: 10.1109/TIP.2009.2018014
[20]
Villena S, Vega M, Babacan S D, et al. Bayesian combination of sparse and non-sparse priors in image super resolution. Digital Signal Process, 2013, 23(2): 530 doi: 10.1016/j.dsp.2012.10.002
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    Received: 30 June 2016 Revised: 30 September 2016 Online: Published: 01 April 2017

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      Shaosheng Dai, Junjie Cui, Dezhou Zhang, Qin Liu, Xiaoxiao Zhang. Study on infrared image super-resolution reconstruction based on an improved POCS algorithm[J]. Journal of Semiconductors, 2017, 38(4): 044010. doi: 10.1088/1674-4926/38/4/044010 S S Dai, J J Cui, D Z Zhang, Q Liu, X X Zhang. Study on infrared image super-resolution reconstruction based on an improved POCS algorithm[J]. J. Semicond., 2017, 38(4): 044010. doi: 10.1088/1674-4926/38/4/044010.Export: BibTex EndNote
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      Shaosheng Dai, Junjie Cui, Dezhou Zhang, Qin Liu, Xiaoxiao Zhang. Study on infrared image super-resolution reconstruction based on an improved POCS algorithm[J]. Journal of Semiconductors, 2017, 38(4): 044010. doi: 10.1088/1674-4926/38/4/044010

      S S Dai, J J Cui, D Z Zhang, Q Liu, X X Zhang. Study on infrared image super-resolution reconstruction based on an improved POCS algorithm[J]. J. Semicond., 2017, 38(4): 044010. doi: 10.1088/1674-4926/38/4/044010.
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      Study on infrared image super-resolution reconstruction based on an improved POCS algorithm

      doi: 10.1088/1674-4926/38/4/044010
      Funds:

      the Natural Science Foundation of Chongqing Science and Technology Commission CSTC2015JCYJA40032

      Project supported by the National Natural Science Foundation of China (Nos. 61275099, 61671094) and the Natural Science Foundation of Chongqing Science and Technology Commission (No. CSTC2015JCYJA40032)

      the National Natural Science Foundation of China 61275099

      the National Natural Science Foundation of China 61671094

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      • Corresponding author: Junjie Cui, Email: cuijunj@163.com
      • Received Date: 2016-06-30
      • Revised Date: 2016-09-30
      • Published Date: 2017-04-01

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