Image Interpolation Based on Spiking Neural Network Model
Abstract
:1. Introduction
2. Image Interpolation
3. Conductance-Based Integrate-and-Fire Neuron Model
4. Proposed Method
4.1. Proposed SNN Model for Edge Detection
4.2. Image Interpolation with SNN-Based Edge Detection
5. Experimental Results
6. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Pramunendar, R.A.; Wibirama, S.; Santosa, P.I. Fish Classification Based on Underwater Image Interpolation and Back-propagation Neural Network. In Proceedings of the 2019 5th International Conference on Science and Technology, ICST 2019, Yogyakarta, Indonesia, 30–31 July 2019. [Google Scholar]
- Moraes, T.; Amorim, P.; Da Silva, J.V.; Pedrini, H. Medical Image Interpolation Based on 3D Lanczos Filtering. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 2020, 8, 294–300. [Google Scholar] [CrossRef]
- Cardona, J.G.; Ortega, A.; Rodriguez-Alvarez, N. Graph-Based Interpolation for Remote Sensing Data. In Proceedings of the 2022 30th European Signal Processing Conference (EUSIPCO), Belgrade, Serbia, 29 August–2 September 2022; pp. 1791–1795. [Google Scholar]
- Saharia, C.; Ho, J.; Chan, W.; Salimans, T.; Fleet, D.J.; Norouzi, M. Image Super-Resolution via Iterative Refinement. IEEE Trans. Pattern Anal. Mach. Intell. 2022, 1–14. [Google Scholar] [CrossRef] [PubMed]
- Lugmayr, A.; Danelljan, M.; Van Gool, L.; Timofte, R. SRFlow: Learning the Super-Resolution Space with Normalizing Flow. In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Springer Science + Business Media: Berlin, Germany, 2020; Volume 12350. [Google Scholar]
- Mei, Y.; Fan, Y.; Zhou, Y. Image Super-Resolution with Non-Local Sparse Attention. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Virtual, 19–25 June 2021. [Google Scholar]
- Wang, P.; Bayram, B.; Sertel, E. A Comprehensive Review on Deep Learning Based Remote Sensing Image Super-Resolution Methods. Earth-Sci. Rev. 2022, 232, 104110. [Google Scholar] [CrossRef]
- Hossain, M.S.; Jalab, H.A.; Kahtan, H.; Abdullah, A. Image Resolution Enhancement Using Improved Edge Directed Interpolation Algorithm. In Proceedings of the 9th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2019, Penang, Malaysia, 29 November–1 December 2019. [Google Scholar]
- Wei, Z.; Ma, K.K. Contrast-Guided Image Interpolation. IEEE Trans. Image Process. 2013, 22, 4271–4285. [Google Scholar] [CrossRef] [PubMed]
- Ye, W.; Ma, K.K. Convolutional Edge Diffusion for Fast Contrast-Guided Image Interpolation. IEEE Signal Process. Lett. 2016, 23, 1260–1264. [Google Scholar] [CrossRef]
- Zhong, B.; Ma, K.K.; Lu, Z. Predictor-Corrector Image Interpolation. J. Vis. Commun. Image Represent. 2019, 61, 50–60. [Google Scholar] [CrossRef]
- Zhao, Y.; Huang, Q. Image Enhancement of Robot Welding Seam Based on Wavelet Transform and Contrast Guidance. Int. J. Innov. Comput. Inf. Control 2022, 18, 149–159. [Google Scholar] [CrossRef]
- Lama, R.K.; Shin, S.; Kang, M.; Kwon, G.R.; Choi, M.R. Interpolation Using Wavelet Transform and Discrete Cosine Transform for High Resolution Display. In Proceedings of the 2016 IEEE International Conference on Consumer Electronics, ICCE 2016, Las Vegas, NV, USA, 7–11 January 2016. [Google Scholar]
- Jia, Z.; Huang, Q. Image Interpolation with Regional Gradient Estimation. Appl. Sci. 2022, 12, 7359. [Google Scholar] [CrossRef]
- Pratt, W.K. Digital Image Processing, 4th Edition. J. Electron. Imaging 2007, 16, 029901. [Google Scholar] [CrossRef]
- Singh, A.; Singh, J. Review and Comparative Analysis of Various Image Interpolation Techniques. In Proceedings of the 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2019, Kannur, India, 5–6 July 2019; pp. 1214–1218. [Google Scholar]
- Palconit, M.G.B.; Conception, R.S.; Alejandrino, J.D.; Evangelista, I.R.S.; Sybingco, E.; Vicerra, R.R.P.; Bandala, A.A.; Dadios, E.P. Counting of Uneaten Floating Feed Pellets in Water Surface Images Using ACF Detector and Sobel Edge Operator. In Proceedings of the IEEE Region 10 Humanitarian Technology Conference, R10-HTC, Bangalore, India, 30 September–2 October 2021; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2021. [Google Scholar]
- Wu, F.; Zhu, C.; Xu, J.; Bhatt, M.W.; Sharma, A. Research on Image Text Recognition Based on Canny Edge Detection Algorithm and K-Means Algorithm. Int. J. Syst. Assur. Eng. Manag. 2022, 13, 72–80. [Google Scholar] [CrossRef]
- Chandwadkar, R.; Dhole, S.; Gadewar, V.; Raut, D.; Tiwaskar, P.S.A. Comparison of Edge Detection Techniques. In Proceedings of the Sixth IRAJ International Conference, Pune, India, 6 October 2013; pp. 133–136. [Google Scholar]
- Keil, M.S.; Cristóbal, G.; Neumann, H. Gradient Representation and Perception in the Early Visual System—A Novel Account of Mach Band Formation. Vision Res. 2006, 46, 2659–2674. [Google Scholar] [CrossRef] [PubMed]
- Manjunath, B.S.; Chellappa, R. A Unified Approach to Boundary Perception: Edges, Textures, and Illusory Contours. IEEE Trans. Neural Netw. 1993, 4, 96–108. [Google Scholar] [CrossRef]
- Natschläger, T. Spatial and Temporal Pattern Analysis via Spiking Neurons. Netw. Comput. Neural Syst. 1998, 9, 319–332. [Google Scholar] [CrossRef]
- Buhmann, J.M.; Lange, T.; Ramacher, U. Image Segmentation by Networks of Spiking Neurons. Neural Comput. 2005, 17, 1010–1031. [Google Scholar] [CrossRef]
- Ghosh-Dastidar, S.; Adeli, H. Spiking Neural Networks. Int. J. Neural Syst. 2009, 19, 295–308. [Google Scholar] [CrossRef] [PubMed]
- Wu, Q.X.; McGinnity, M.; Maguire, L.; Belatreche, A.; Glackin, B. Edge Detection Based on Spiking Neural Network Model. In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Springer Science + Business Media: Berlin, Germany, 2007; Volume 4682. [Google Scholar]
- Clogenson, M.; Kerr, D.; McGinnity, M.; Coleman, S.; Wu, Q. Biologically Inspired Edge Detection Using Spiking Neural Networks and Hexagonal Images. In Proceedings of the International Conference on Neural Computation Theory and Applications, Paris, France, 24–26 October 2011. [Google Scholar]
- Kerr, D.; Coleman, S.; McGinnity, M.; Wu, Q.X.; Clogenson, M. Biologically Inspired Edge Detection. In Proceedings of the International Conference on Intelligent Systems Design and Applications, ISDA, Cordoba, Spain, 22–24 November 2011. [Google Scholar]
- Kerr, D.; McGinnity, M.; Coleman, S.; Wu, Q.; Clogenson, M. Spiking Hierarchical Neural Network for Corner Detection. In Proceedings of the International Conference on Neural Computation Theory and Applications, Paris, France, 24–26 October 2011; pp. 230–235. [Google Scholar]
- Wu, Q.X.; McGinnity, T.M.; Maguire, L.; Cai, R.; Chen, M. A Visual Attention Model Based on Hierarchical Spiking Neural Networks. Neurocomputing 2013, 116, 3–12. [Google Scholar] [CrossRef]
- Kerr, D.; Coleman, S.; McGinnity, M.T. Biologically Inspired Intensity and Depth Image Edge Extraction. IEEE Trans. Neural Netw. Learn. Syst. 2018, 29, 5356–5365. [Google Scholar] [CrossRef] [PubMed]
- Kerr, D.; Coleman, S.A.; McGinnity, T.M.; Clogenson, M. Biologically Inspired Intensity and Range Image Feature Extraction. In Proceedings of the International Joint Conference on Neural Networks, Dallas, TX, USA, 4–9 August 2013. [Google Scholar]
- Kerr, D.; McGinnity, T.M.; Coleman, S.; Clogenson, M. A Biologically Inspired Spiking Model of Visual Processing for Image Feature Detection. Neurocomputing 2015, 158, 268–280. [Google Scholar] [CrossRef]
- Yedjour, H.; Meftah, B.; Lézoray, O.; Benyettou, A. Edge Detection Based on Hodgkin–Huxley Neuron Model Simulation. Cogn. Process. 2017, 18, 315–323. [Google Scholar] [CrossRef]
- Hodgkin, A.L.; Huxley, A.F. A Quantitative Description of Membrane Current and Its Application to Conduction and Excitation in Nerve. J. Physiol. 1952, 117, 500–544. [Google Scholar] [CrossRef] [PubMed]
- Vemuru, K.V. Image Edge Detector with Gabor Type Filters Using a Spiking Neural Network of Biologically Inspired Neurons. Algorithms 2020, 13, 165. [Google Scholar] [CrossRef]
- İncetaş, M.O. Anisotropic Diffusion Filter Based on Spiking Neural Network Model. Arab. J. Sci. Eng. 2022, 47, 9849–9860. [Google Scholar] [CrossRef]
- Fitzhugh, R. Mathematical Models of Excitation and Propagation in Nerve. Biol. Eng. 1969, 9, 1–85. [Google Scholar]
- Nagumo, J.; Arimoto, S.; Yoshizawa, S. An Active Pulse Transmission Line Simulating Nerve Axon*. Proc. IRE 1962, 50, 2061–2070. [Google Scholar] [CrossRef]
- Izhikevich, E.M. Simple Model of Spiking Neurons. IEEE Trans. Neural Netw. 2003, 14, 1569–1572. [Google Scholar] [CrossRef] [PubMed]
- Destexhe, A. Conductance-Based Integrate-and-Fire Models. Neural Comput. 1997, 9, 503–514. [Google Scholar] [CrossRef]
- Wu, Q.X.; McGinnity, M.; Maguire, L.; Glackin, B.; Belatreche, A. Learning Mechanisms in Networks of Spiking Neurons. Stud. Comput. Intell. 2006, 35, 171–197. [Google Scholar] [CrossRef]
- Bull, D.R. Communicating Pictures: A Course in Image and Video Coding; Academic Press: Cambridge, MA, USA, 2014; ISBN 9780080993744. [Google Scholar]
- Wang, Z.; Bovik, A.C.; Sheikh, H.R.; Simoncelli, E.P. Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Trans. Image Process. 2004, 13, 600–612. [Google Scholar] [CrossRef] [Green Version]
Image | CGI | CED | PCI | IEDI | WTCGI | GEI | Proposed |
---|---|---|---|---|---|---|---|
Bike | 25.82 | 25.82 | 25.90 | 25.17 | 25.21 | 25.85 | 26.60 |
Wheel | 21.01 | 20.98 | 21.22 | 20.31 | 20.57 | 21.32 | 21.53 |
Boats | 29.51 | 29.56 | 29.77 | 29.24 | 29.32 | 29.42 | 29.71 |
Butterfly | 29.27 | 29.24 | 29.31 | 28.97 | 28.97 | 29.26 | 29.55 |
House | 32.83 | 32.71 | 32.88 | 32.31 | 31.87 | 32.84 | 33.17 |
Cameraman | 25.86 | 25.9 | 25.81 | 25.48 | 25.76 | 25.83 | 26.09 |
Baboon | 22.50 | 22.41 | 22.53 | 22.41 | 22.35 | 22.59 | 22.82 |
Peppers | 30.88 | 30.77 | 30.87 | 30.47 | 30.19 | 30.81 | 31.12 |
Fence | 25.70 | 25.63 | 25.84 | 25.61 | 25.69 | 25.75 | 26.01 |
Airplane | 26.54 | 26.49 | 26.59 | 26.6 | 26.10 | 26.61 | 26.88 |
Barbara | 23.75 | 23.64 | 23.82 | 23.54 | 23.41 | 24.01 | 24.25 |
Stars | 34.13 | 33.94 | 34.38 | 33.36 | 33.71 | 34.33 | 34.67 |
Average | 27.32 | 27.26 | 27.41 | 26.96 | 26.93 | 27.39 | 27.70 |
Image | CGI | CED | PCI | IEDI | WTCGI | GEI | Proposed |
---|---|---|---|---|---|---|---|
Bike | 0.8808 | 0.8812 | 0.8803 | 0.8751 | 0.8791 | 0.8798 | 0.9071 |
Wheel | 0.8621 | 0.8626 | 0.8668 | 0.8644 | 0.8649 | 0.8665 | 0.8986 |
Boats | 0.8763 | 0.8801 | 0.8794 | 0.8771 | 0.8744 | 0.8796 | 0.8963 |
Butterfly | 0.9721 | 0.9732 | 0.9720 | 0.9718 | 0.9698 | 0.9758 | 0.9992 |
House | 0.8781 | 0.8778 | 0.8789 | 0.8783 | 0.8775 | 0.8780 | 0.8956 |
Cameraman | 0.8711 | 0.8732 | 0.8715 | 0.8704 | 0.8692 | 0.8732 | 0.8976 |
Baboon | 0.9125 | 0.9111 | 0.9130 | 0.9121 | 0.9112 | 0.9165 | 0.9403 |
Peppers | 0.9032 | 0.9041 | 0.9035 | 0.9029 | 0.9026 | 0.9025 | 0.9278 |
Fence | 0.7752 | 0.7780 | 0.7785 | 0.7763 | 0.7765 | 0.7723 | 0.7893 |
Airplane | 0.9405 | 0.9410 | 0.9401 | 0.9389 | 0.9422 | 0.9412 | 0.9591 |
Barbara | 0.9125 | 0.9128 | 0.9130 | 0.9114 | 0.9105 | 0.9118 | 0.9392 |
Stars | 0.9584 | 0.9603 | 0.9617 | 0.9608 | 0.9610 | 0.9608 | 0.9762 |
Average | 0.8952 | 0.8963 | 0.8966 | 0.8950 | 0.8949 | 0.8965 | 0.9279 |
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İncetaş, M.O. Image Interpolation Based on Spiking Neural Network Model. Appl. Sci. 2023, 13, 2438. https://doi.org/10.3390/app13042438
İncetaş MO. Image Interpolation Based on Spiking Neural Network Model. Applied Sciences. 2023; 13(4):2438. https://doi.org/10.3390/app13042438
Chicago/Turabian Styleİncetaş, Mürsel Ozan. 2023. "Image Interpolation Based on Spiking Neural Network Model" Applied Sciences 13, no. 4: 2438. https://doi.org/10.3390/app13042438
APA Styleİncetaş, M. O. (2023). Image Interpolation Based on Spiking Neural Network Model. Applied Sciences, 13(4), 2438. https://doi.org/10.3390/app13042438