GOA-ISR: A Grasshopper Optimization Algorithm for Improved Image Super-Resolution
محورهای موضوعی : Image Processing
Bahar Ghaderi
1
,
Hamid Azad
2
,
Hamed Agahi
3
1 - Department of Electrical-Telecommunication Engineering,Faculty of Engineering Shiraz Branch ,Islamic Azad University,Shiraz,Iran
2 - Department of Electrical-Telecommunication Engineering,Faculty of Engineering Shiraz Branch ,Islamic Azad University,Shiraz,Iran
3 - Department of Electrical-Telecommunication Engineering,Faculty of Engineering Shiraz Branch ,Islamic Azad University,Shiraz,Iran
کلید واژه: Super Resolution (SR), High-Resolution (HR), Low-Resolution (LR), Learning-based Methods, Grasshopper Optimization Algorithm (GOA).,
چکیده مقاله :
The image super-resolution (ISR) process provides a high-resolution (HR) image from an input low-resolution (LR) image. This process is an important and challenging issue in computer vision and image processing. Various methods are used for ISR, that learning-based methods are one of the most widely used methods in this field. In this approach, a set of training images is used in various learning based ISR methods to reconstruct the input LR image. To this end, appropriate reconstruction weights for the image must be computed. In general, the least-squares estimation (LSE) approach is used for obtaining optimal reconstruction weights. The accuracy of SR depends on the effectiveness of minimizing the LSE problem. Therefore, it is still a challenge to obtain more accurate reconstruction weights for better SR processing. In this study, a Grasshopper Optimization Algorithm (GOA)-based ISR method (GOA-ISR) is proposed in order to minimize the LSE problem more effectively. A new formulation for the upper bound and the lower bound is introduced to make the search process of the GOA algorithm suitable for ISR. The simulation results on DIVerse 2K (DIV2K) dataset, URBAN100, BSD100, Set 14 and Set 5 datasets affirm the advantage of the proposed GOA-ISR approach in comparison with some other basic Neighbor Embedding (NE), Sparse Coding (SC), Adaptive Sparse, Iterative Kernel Correction (IKC), Second-order Attention Network (SAN), Sparse Neighbor Embedding and Grey Wolf Optimizer (GWO) methods in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The results of the experiments show the superiority of the proposed method comparing to the best compared method (DWSR) increases 8.613 % PSNR.
The image super-resolution (ISR) process provides a high-resolution (HR) image from an input low-resolution (LR) image. This process is an important and challenging issue in computer vision and image processing. Various methods are used for ISR, that learning-based methods are one of the most widely used methods in this field. In this approach, a set of training images is used in various learning based ISR methods to reconstruct the input LR image. To this end, appropriate reconstruction weights for the image must be computed. In general, the least-squares estimation (LSE) approach is used for obtaining optimal reconstruction weights. The accuracy of SR depends on the effectiveness of minimizing the LSE problem. Therefore, it is still a challenge to obtain more accurate reconstruction weights for better SR processing. In this study, a Grasshopper Optimization Algorithm (GOA)-based ISR method (GOA-ISR) is proposed in order to minimize the LSE problem more effectively. A new formulation for the upper bound and the lower bound is introduced to make the search process of the GOA algorithm suitable for ISR. The simulation results on DIVerse 2K (DIV2K) dataset, URBAN100, BSD100, Set 14 and Set 5 datasets affirm the advantage of the proposed GOA-ISR approach in comparison with some other basic Neighbor Embedding (NE), Sparse Coding (SC), Adaptive Sparse, Iterative Kernel Correction (IKC), Second-order Attention Network (SAN), Sparse Neighbor Embedding and Grey Wolf Optimizer (GWO) methods in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The results of the experiments show the superiority of the proposed method comparing to the best compared method (DWSR) increases 8.613 % PSNR.
[1] B. Ghaderi and H. Azad, "Deep Learning Algorithms in Super-Resolution Images," Journal of Circuits, Data and Systems Analysis, vol. 1, no. 1, p. 47, 2023.
[2] P. Behjati, P. Rodriguez, C. Fernández, I. Hupont, A. Mehri, and J. Gonzàlez, "Single image super-resolution based on directional variance attention network," Pattern Recognition, vol. 133, p. 108997, 2023.
[3] T. Goto, T. Fukuoka, F. Nagashima, S. Hirano, and M. Sakurai, "Super-resolution System for 4K-HDTV," in 2014 22nd International Conference on Pattern Recognition, 2014: IEEE, pp. 4453-4458.
[4] A. Rapuano, G. Iovane, and M. Chinnici, "A scalable Blockchain based system for super resolution images manipulation," in 2020 IEEE 6th International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application (DependSys), 2020: IEEE, pp. 8-15.
[5] H. Dastmalchi and H. Aghaeinia, "Super-resolution of very low-resolution face images with a wavelet integrated, identity preserving, adversarial network," Signal Processing: Image Communication, p. 116755, 2022.
[6] I. Taghavi et al., "Ultrasound super-resolution imaging with a hierarchical Kalman tracker," Ultrasonics, vol. 122, p. 106695, 2022.
[7] P. Wang, B. Bayram, and E. Sertel, "A comprehensive review on deep learning based remote sensing image super-resolution methods," Earth-Science Reviews, p. 104110, 2022.
[8] K. Zhu, H. Guo, S. Li, and X. Lin, "Online tool wear monitoring by super-resolution based machine vision," Computers in Industry, vol. 144, p. 103782, 2023.
[9] H. Hou and H. Andrews, "Cubic splines for image interpolation and digital filtering," IEEE Transactions on acoustics, speech, and signal processing, vol. 26, no. 6, pp. 508-517, 1978.
[10] M. Li and T. Q. Nguyen, "Markov random field model-based edge-directed image interpolation," IEEE Transactions on Image Processing, vol. 17, no. 7, pp. 1121-1128, 2008.
[11] J. Sun, J. Zhu, and M. F. Tappen, "Context-constrained hallucination for image super-resolution," in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010: IEEE, pp. 231-238.
[12] L. Wang, S. Xiang, G. Meng, H. Wu, and C. Pan, "Edge-directed single-image super-resolution via adaptive gradient magnitude self-interpolation," IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 8, pp. 1289-1299, 2013.
[13] W. T. Freeman, T. R. Jones, and E. C. Pasztor, "Example-based super-resolution," IEEE Computer graphics and Applications, vol. 22, no. 2, pp. 56-65, 2002.
[14] C. Dong, C. C. Loy, K. He, and X. Tang, "Learning a deep convolutional network for image super-resolution," in European conference on computer vision, 2014: Springer, pp. 184-199.
[15] K. Zhang, J. Li, H. Wang, X. Liu, and X. Gao, "Learning local dictionaries and similarity structures for single image super-resolution," Signal Processing, vol. 142, pp. 231-243, 2018.
[16] N. Kumar and A. Sethi, "Fast learning-based single image super-resolution," IEEE Transactions on Multimedia, vol. 18, no. 8, pp. 1504-1515, 2016.
[17] J. Jiang, C. Wang, X. Liu, and J. Ma, "Deep learning-based face super-resolution: A survey," ACM Computing Surveys (CSUR), vol. 55, no. 1, pp. 1-36, 2021.
[18] P. P. Gajjar and M. V. Joshi, "New learning based super-resolution: use of DWT and IGMRF prior," IEEE Transactions on Image Processing, vol. 19, no. 5, pp. 1201-1213, 2010.
[19] S. S. Rajput, V. K. Bohat, and K. Arya, "Grey wolf optimization algorithm for facial image super-resolution," Applied Intelligence, vol. 49, no. 4, pp. 1324-1338, 2019.
[20] K. Nguyen, C. Fookes, S. Sridharan, M. Tistarelli, and M. Nixon, "Super-resolution for biometrics: A comprehensive survey," Pattern Recognition, vol. 78, pp. 23-42, 2018.
[21] N. Wang, D. Tao, X. Gao, X. Li, and J. Li, "A comprehensive survey to face hallucination," International journal of computer vision, vol. 106, no. 1, pp. 9-30, 2014.
[22] Y. Tang, P. Yan, Y. Yuan, and X. Li, "Single-image super-resolution via local learning," International Journal of Machine Learning and Cybernetics, vol. 2, no. 1, pp. 15-23, 2011.
[23] K. Zhang, X. Gao, D. Tao, and X. Li, "Single image super-resolution with non-local means and steering kernel regression," IEEE Transactions on Image Processing, vol. 21, no. 11, pp. 4544-4556, 2012.
[24] K. Zhang, X. Gao, D. Tao, and X. Li, "Single image super-resolution with multiscale similarity learning," IEEE transactions on neural networks and learning systems, vol. 24, no. 10, pp. 1648-1659, 2013.
[25] Z. Wang, Y. Yang, Z. Wang, S. Chang, J. Yang, and T. S. Huang, "Learning super-resolution jointly from external and internal examples," IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 4359-4371, 2015.
[26] X. Lu, H. Yuan, Y. Yuan, P. Yan, L. Li, and X. Li, "Local learning-based image super-resolution," in 2011 IEEE 13th International Workshop on Multimedia Signal Processing, 2011: IEEE, pp. 1-5.
[27] L. An and B. Bhanu, "Image super-resolution by extreme learning machine," in 2012 19th IEEE international conference on image processing, 2012: IEEE, pp. 2209-2212.
[28] M. Bevilacqua, A. Roumy, C. Guillemot, and M. L. Alberi-Morel, "Low-complexity single-image super-resolution based on nonnegative neighbor embedding," 2012.# [29] S. Saremi, S. Mirjalili, and A. Lewis, "Grasshopper optimisation algorithm: theory and application," Advances in engineering software, vol. 105, pp. 30-47, 2017.
[30] I. J. Cox, M. L. Miller, J. A. Bloom, and C. Honsinger, Digital watermarking. Springer, 2002.
[31] P. Tumuluru and B. Ravi, "GOA-based DBN: Grasshopper optimization algorithm-based deep belief neural networks for cancer classification," International Journal of Applied Engineering Research, vol. 12, no. 24, pp. 14218-14231, 2017.
[32] P.-H. Dinh, "A novel approach based on grasshopper optimization algorithm for medical image fusion," Expert Systems with Applications, vol. 171, p. 114576, 2021.
[33] J. H. Holland, "Genetic algorithms," Scientific american, vol. 267, no. 1, pp. 66-73, 1992.
[34] R. Eberhart and J. Kennedy, "A new optimizer using particle swarm theory," in MHS'95. Proceedings of the sixth international symposium on micro machine and human science, 1995: Ieee, pp. 39-43.
[35] A. Kamalinia and A. Ghaffari, "Hybrid task scheduling method for cloud computing by genetic and PSO algorithms," J. Inf. Syst. Telecommun, vol. 4, pp. 271-281, 2016.
[36] X.-S. Yang, "Firefly algorithms for multimodal optimization," in International symposium on stochastic algorithms, 2009: Springer, pp. 169-178.
[37] A. Mahmoodzadeh, H. Agahi, and M. Salehi, "Handwritten Digits Recognition Using an Ensemble Technique Based on the Firefly Algorithm," Journal of Information Systems and Telecommunication (JIST), vol. 3, no. 23, p. 136, 2019.
[38] X.-S. Yang, "A new metaheuristic bat-inspired algorithm," in Nature inspired cooperative strategies for optimization (NICSO 2010): Springer, 2010, pp. 65-74.
[39] E. Rashedi, E. Rashedi, and H. Nezamabadi-Pour, "A comprehensive survey on gravitational search algorithm," Swarm and evolutionary computation, vol. 41, pp. 141-158, 2018.
[40] M. Tourani, "Improvement of Firefly Algorithm using Particle Swarm Optimization and Gravitational Search Algorithm," Journal of Information Systems and Telecommunication (JIST), vol. 2, no. 34, p. 123, 2021.
[41] C. M. Topaz, A. J. Bernoff, S. Logan, and W. Toolson, "A model for rolling swarms of locusts," The European Physical Journal Special Topics, vol. 157, no. 1, pp. 93-109, 2008.
[42] S. M. Rogers, T. Matheson, E. Despland, T. Dodgson, M. Burrows, and S. J. Simpson, "Mechanosensory-induced behavioural gregarization in the desert locust Schistocerca gregaria," Journal of Experimental Biology, vol. 206, no. 22, pp. 3991-4002, 2003.
[43]Y. ChangH, "XiongY. Super-resolutionthroughneighborembedding," Proceedingsofthe2004IEEEComputer Society C o nference on ComputerVision and Pattern Rec ogni—tion, pp. 275-282, 2004.
[44]J. Yang, J. Wright, T. S. Huang, and Y. Ma, "Image super-resolution via sparse representation," IEEE transactions on image processing, vol. 19, no. 11, pp. 2861-2873, 2010.
[45]J. Gu, H. Lu, W. Zuo, and C. Dong, "Blind super-resolution with iterative kernel correction," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 1604-1613.
[46]X. Gao, K. Zhang, D. Tao, and X. Li, "Image super-resolution with sparse neighbor embedding," IEEE Transactions on Image Processing, vol. 21, no. 7, pp. 3194-3205, 2012.
[47]W. Dong, L. Zhang, G. Shi, and X. Wu, "Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization," IEEE Transactions on image processing, vol. 20, no. 7, pp. 1838-1857, 2011.
[48]T. Dai, J. Cai, Y. Zhang, S.-T. Xia, and L. Zhang, "Second-order attention network for single image super-resolution," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 11065-11074.
[49]E. Agustsson and R. Timofte, "Ntire 2017 challenge on single image super-resolution: Dataset and study," in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2017, pp. 126-135.
[50]F. Akhlaghian Tab, K. Ghaderi, and P. Moradi, "A New Robust Digital Image Watermarking Algorithm Based on LWT-SVD and Fractal Images," Journal of Information Systems and Telecommunication (JIST), vol. 1, no. 9, p. 1, 2015.
[51]K. Li, S. Yang, R. Dong, X. Wang, and J. Huang, "Survey of single image super‐resolution reconstruction," IET Image Processing, vol. 14, no. 11, pp. 2273-2290, 2020.
[52]V. K. Bohat and K. Arya, "An effective gbest-guided gravitational search algorithm for real-parameter optimization and its application in training of feedforward neural networks," Knowledge-Based Systems, vol. 143, pp. 192-207, 2018.
[53] S.-C. Chu, Z.-C. Dou, J.-S. Pan, L. Kong, V. Snášel, and J. Watada, "DWSR: an architecture optimization framework for adaptive super-resolution neural networks based on meta-heuristics," Artificial Intelligence Review, vol. 57, no. 2, p. 23, 2024.
[54] Y. Meraihi, A. B. Gabis, S. Mirjalili, and A. Ramdane-Cherif, "Grasshopper optimization algorithm: theory, variants, and applications," IEEE Access, vol. 9, pp. 50001-50024, 2021.
[55] J.-B. Huang, A. Singh, and N. Ahuja, "Single image super-resolution from transformed self-exemplars," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 5197-5206.
[56] D. Martin, C. Fowlkes, D. Tal, and J. Malik, "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics," in Proceedings eighth IEEE international conference on computer vision. ICCV 2001, 2001, vol. 2: IEEE, pp. 416-423.
[57] R. Zeyde, M. Elad, and M. Protter, "On single image scale-up using sparse-representations," in Curves and Surfaces: 7th International Conference, Avignon, France, June 24-30, 2010, Revised Selected Papers 7, 2012: Springer, pp. 711-730.
