Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

Spatial general autoregressive model-based image interpolation accommodates arbitrary scale factors

1 Nanjing Institute of Technology, Kangni Institute of Industrial Science and Technology, Nanjing 211167, China
2 School of Mechanical Engineering, Nanjing Institute of Technology, Nanjing 211167, China
3 School of Mechanical Engineering, Southeast University, Nanjing 211189, China

Special Issues: Recent Achievements in Multimedia Data Processing

This paper proposed a novel image interpolation algorithm with an arbitrary upscaling factor based on the spatial general autoregressive model. First, to accommodate arbitrary scale factors, a non-integer mapping method was modulated into the spatial general autoregressive model, which was employed to model the piecewise stationary pattern with a higher description capacity than autoregressive models. A gradient angle guided extension method was utilized to extend the spatial general autoregressive model, and more pixels in the neighborhood were included to estimate the parameters of the spatial general autoregressive model. To realize the high-accuracy estimation of the model parameters, a regularization method via an elastic network was adopted to maintain the complexity of the object function in a reasonable state and address the overfitting problem. We also introduced an iterative curvature method to refine the interpolation result of those image blocks with large variances of gray intensities. Experiments on 25 images were conducted with integer and non-integer magnification factors to systematically verify the objective and subjective measures of the proposed method. The visual artifacts were effectively suppressed by the proposed method, and a flexible interpolation method for arbitrary scale factors was implemented.
  Article Metrics

Keywords image processing; arbitrary scale interpolation; autoregressive; gradient adaptive extension; elastic network; iterative curvature

Citation: Yuntao Hu, Fei Hao, Chao Meng, Lili Sun, Dashuai Xu, Tianqi Zhang. Spatial general autoregressive model-based image interpolation accommodates arbitrary scale factors. Mathematical Biosciences and Engineering, 2020, 17(6): 6573-6600. doi: 10.3934/mbe.2020343


  • 1. Y. Tian, M. M. Kaleemullah, M. A. Rodhaan, B. Song, A. Al-Dhelaan, T. Ma, A privacy preserving location service for cloud-of-things system, J. Parallel Distrib. Comput., 123 (2019), 215-222.
  • 2. W. Wang, W. Zhang, Z. Jin, K. Sun, R. Zou, C. Huang, et al., A Novel Location Privacy Protection Scheme with Generative Adversarial Network, International Conference on Big Data and Security Springer, 2019.
  • 3. L. Wang, X. Shu, W. Zhang, Y. Chen, Design and Optimization of Evaluation Metrics in Object Detection and Tracking for Low-Altitude Aerial Video, International Conference on Big Data and Security, 2019.
  • 4. Y. Liu, M. Pang, Research on Medical Image Encryption Method Based on Chaotic Scrambling and Compressed Sensing, International Conference on Big Data and Security, 2019.
  • 5. B. Song, M. M. Hassan, A. Alamri, A. Alelaiwi, Y. Tian, M. Pathan, et al., A two-stage approach for task and resource management in multimedia cloud environment, Computing, 98 (2016), 119-145.
  • 6. Z. Pan, C. N. Yang, V. S. Sheng, N. Xiong, W. Meng, Machine Learning for Wireless Multimedia Data Security, Sec. Commun. Networks, 2019 (2019), 7682306.
  • 7. S. Ousguine, F. Essannouni, L. Essannouni, M. Abbad, D. Aboutajdine, A New Image Interpolation Using Laplacian Operator, International Symposium on Ubiquitous Networking, 2016, Singapore.
  • 8. S. Pan, L. Han, Y. Tao, Q. Liu, Study on Indicator Recognition Method of Water Meter Based on Convolution Neural Network, International Conference on Big Data and Security, 2019, Springer.
  • 9. J. A. Parker, R. V. Kenyon, D. E. Troxel, Comparison of Interpolating Methods for Image Resampling, IEEE Trans. Med. Imaging, 2 (1983), 31-39.
  • 10. R. Hanssen, R. Bamler, Evaluation of interpolation kernels for SAR interferometry, IEEE Trans. Geosci. Remote Sens., 37 (1999), 318-321.
  • 11. T. M. Lehmann, C. Gonner, K. Spitzer, Survey: interpolation methods in medical image processing, IEEE Trans. Med. Imaging, 18 (1999), 1049-1075.
  • 12. D. Y. Han, Comparison of Commonly Used Image Interpolation Methods, Proceedings of the 2nd international conference on computer science and electronics engineering, Atlantis Press, 2013.
  • 13. A. Amanatiadis, I. Andreadis, Performance evaluation techniques for image scaling algorithms, IEEE International Workshop on Imaging Systems and Techniques, 2008.
  • 14. Y. Li, F. Qi, Y. Wan, Improvements On Bicubic Image Interpolation, 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2019.
  • 15. H. Hsieh, H. Andrews, Cubic splines for image interpolation and digital filtering, IEEE Trans. Acoust. Speech Signal Process., 26 (1978), 508-517.
  • 16. S. Abbas, M. Irshad, M. Z. Hussain, Adaptive image interpolation technique based on cubic trigonometric B-spline representation, IET Image Process., 12 (2018), 769-777.
  • 17. D. D. Muresan, T. W. Parks, Adaptively quadratic (AQua) image interpolation, IEEE Trans. Image Process., 13 (2004), 690-698.
  • 18. C. Chen, C. Lai, Iterative Linear Interpolation Based on Fuzzy Gradient Model for Low-Cost VLSI Implementation, IEEE Trans. VLSI Syst., 22 (2014), 1526-1538.
  • 19. Q. Wang and R. K. Ward, A New Orientation-Adaptive Interpolation Method, IEEE Trans. Image Process., 16 (2007), 889-900.
  • 20. C. M. Zwart, D. H. Frakes, Segment Adaptive Gradient Angle Interpolation, IEEE Trans. Image Process., 22 (2013), 2960-2969.
  • 21. X. Li, M. T. Orchard, New edge-directed interpolation, IEEE Trans. Image Process., 10 (2001), 1521-1527.
  • 22. X. Zhang, X. Wu, Image Interpolation by Adaptive 2-D Autoregressive Modeling and Soft-Decision Estimation, IEEE Trans. Image Process., 17 (2008), 887-896.
  • 23. Q. Wang, J. Liu, W. Yang, Z. Guo, Adaptive autoregressive model with window extension via explicit geometry for image interpolation, IEEE International Conference on Image Processing (ICIP), 2015.
  • 24. W. Yang, J. Liu, S. Yang, Z. Guo, Novel autoregressive model based on adaptive window-extension and patch-geodesic distance for image interpolation, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015.
  • 25. W. H. Yang, J. Y. Liu, M. D. Li, Z. Gao, Isophote-Constrained Autoregressive Model With Adaptive Window Extension for Image Interpolation, IEEE Trans. Circuits Syst. Video Technol., 28 (2018), 1071-1086.
  • 26. F. Hao, J. Shi, R. Chen, S, Zhu, Z. Zhang, Noise-insensitive and edge-preserving resolution upconversion scheme for digital image based on the spatial general autoregressive model, IET Image Process., 10 (2016), 280-288.
  • 27. K. Chang, P. L. K. Ding, B. Li, Single Image Super-resolution Using Collaborative Representation and Non-local Self-Similarity, Signal Process., 149 (2018), 49-61.
  • 28. D. Cheng, K. I. Kou, FFT Multichannel Interpolation and Application to Image Super-resolution, Signal Process., 162 (2019), 21-34.
  • 29. A. Giachetti, N. Asuni, Real-Time Artifact-Free Image Upscaling, IEEE Trans. Image Process., 20 (2011), 2760-2768.
  • 30. H. Kim, Y. Cha, S. Kim, Curvature Interpolation Method for Image Zooming, IEEE Trans. Image Process., 20 (2011), 1895-1903.
  • 31. A. Marquina, S. Osher, A New Time Dependent Model Based on Level Set Motion for Nonlinear Deblurring and Noise Removal, International Conference on Scale-Space Theories in Computer Vision, 1999.
  • 32. T. F. Chan, J. J. Shen, Image processing and analysis: variational, PDE, wavelet, and stochastic methods, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 2005.
  • 33. J. Hu, Y. Luo, Single-image superresolution based on local regression and nonlocal self-similarity, J. Electron. Imaging, 23 (2014), 033014.
  • 34. N. Asuni, A. Giachetti, Accuracy Improvements and Artifacts Removal in Edge Based Image Interpolation, VISAPP (1), 8 (2008), 58-65.
  • 35. W. Dong, L. Zhang, R. Lukac, G. Shi, Sparse Representation Based Image Interpolation With Nonlocal Autoregressive Modeling, IEEE Trans. Image Process., 22 (2013), 1382-1394.
  • 36. F. Hao, J. Shi, Z. Zhang, R, Chen, S. Zhu, Canny edge detection enhancement by general auto-regression model and bi-dimensional maximum conditional entropy, Optik, 125 (2014), 3946-3953.
  • 37. M. Li, J. Liu, J. Ren, Z. Guo, Adaptive General Scale Interpolation Based on Weighted Autoregressive Models, IEEE Trans. Circuits Syst. Video Technol., 25 (2015), 200-211.
  • 38. A. Ullah, M. Z. Asghar, A. Habib, S. Aleem, F. M. Kundi, A. M. Khattak, Optimizing the Efficiency of Machine Learning Techniques, International Conference on Big Data and Security, 2019.
  • 39. R. M. Rifkin, R. A. Lippert, Notes on regularized least squares, Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory, 2007.
  • 40. K. Koh, S. J. Kim, S. Boyd, An interior-point method for large-scale l1-regularized logistic regression, J. Mach. Learn. Res., 8 (2007), 1519-1555.
  • 41. J. Friedman, T. Hastie, R. Tibshirani, Regularization paths for generalized linear models via coordinate descent, J. Stat. Software, 33 (2010), 1.
  • 42. H. Wang, W. Guan, K. Zhang, Over-Sampling Multi-classification Method Based on Centroid Space, International Conference on Big Data and Security, 2019.
  • 43. Y. Gong, I. F. Sbalzarini, Curvature filters efficiently reduce certain variational energies, IEEE Trans. Image Process., 26 (2017), 1786-1798.
  • 44. X. Hu, H. Mu, X. Zhang, Z. Wang, T. Tan, J. Sun, Meta-SR: A Magnification-Arbitrary Network for Super-Resolution, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020.


Reader Comments

your name: *   your email: *  

© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved