Single Image Dehaze using Deep Learning with Beetle Swarm Optimization Algorithm
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Abstract
Particles present in the atmosphere the captured light is deviated into scattering due to this, haze is captured by camera. The process of removal of haze in an image is called De-hazing. Dehazing is a challenging task in computer vision and surveillance applications. Deep learning methods have been developed and shown encouraging results. However, these approaches have significant impact on how well these approach work. In this paper we introduce an innovative deep learning as Beetle Swarm Optimization (BSO) algorithm for single image dehazing. BSO is a nature inspired optimization algorithm that uses beetle’s social behavior as model to get the best response. The dehazing model performs more effectively after the parameters of deep learning network optimized using BSO. The experimental results indicate well how our method works at eliminating haze from single images. Benchmark data sets are used to access the suggested strategy. In this paper the proposed method is evaluated in terms of Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity Index (SSI) also our method produces dehazed images with high contrast and colour accuracy as well as more visually pleasing shots. The proposed method which has applications in surveillance, remote sensing and self-driving cars provides a dependable and efficient solution to dehaze a single image.
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S. G. Narasimhan and S. K. Nayar, “Contrast restoration of weather degraded images,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 6, pp. 713–724, 2003, doi: 10.1109/TPAMI.2003.1201821. https://doi.org/10.1109/TPAMI.2003.1201821
S. G. Narasimhan and S. K. Nayar, “Chromatic framework for vision in bad weather,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 1, pp. 598–605, 2000, doi: 10.1109/cvpr.2000.855874. https://doi.org/10.1109/CVPR.2000.855874
R. Prakash Kumar and N. Manja Naik, “A Review on Efficient state-of-The-Art Image Dehazing Techniques,” ICISTSD 2022 - 3rd Int. Conf. Innov. Sci. Technol. Sustain. Dev., no. 2, pp. 115–121, 2022, doi: 10.1109/ICISTSD55159.2022.10010424. https://doi.org/10.1109/ICISTSD55159.2022.10010424
C. A. Hartanto and L. Rahadianti, Single image dehazing using deep learning, vol. 5, no. 1. Springer International Publishing, 2021. doi: 10.30630/joiv.5.1.431. https://doi.org/10.30630/joiv.5.1.431
M. A. N. I. Fahim and H. Y. Jung, “Single image dehazing using end-to-end deep-dehaze network,” ACM Int. Conf. Proceeding Ser., pp. 158–163, 2020, doi: 10.1145/3426020.3426058. https://doi.org/10.1145/3426020.3426058
S. Lee, S. Yun, J. H. Nam, C. S. Won, and S. W. Jung, “A review on dark channel prior based image dehazing algorithms,” Eurasip J. Image Video Process., vol. 2016, no. 1, pp. 1–23, 2016, doi: 10.1186/s13640-016-0104-y. https://doi.org/10.1186/s13640-016-0104-y
H. Zhang, V. Sindagi, and V. M. Patel, “Joint Transmission Map Estimation and Dehazing Using Deep Networks,” IEEE Trans. Circuits Syst. Video Technol., vol. 30, no. 7, pp. 1975–1986, 2020, doi: 10.1109/TCSVT.2019.2912145. https://doi.org/10.1109/TCSVT.2019.2912145
G. Veerappan and C. Suresh Gnana Dhas, “Survey of optimistic power aware routing protocols in mobile datacenter networks,” ARPN J. Eng. Appl. Sci., vol. 10, no. 12, pp. 5406–5412, 2015, doi: 10.1007/978-3-319-46475-6. https://doi.org/10.1007/978-3-319-46475-6
W. Ren, L. Zhou, and J. Chen, “Unsupervised single image dehazing with generative adversarial network,” Multimed. Syst., no. 0123456789, pp. 18–20, 2022, doi: 10.1007/s00530-021-00852-z. https://doi.org/10.1007/s00530-021-00852-z
H. Zhu et al., “Single-Image Dehazing via Compositional Adversarial Network,” IEEE Trans. Cybern., vol. 51, no. 2, pp. 829–838, 2021, doi: 10.1109/TCYB.2019.2955092. https://doi.org/10.1109/TCYB.2019.2955092
J. Li, G. Li, and H. Fan, “Image Dehazing Using Residual-Based Deep CNN,” IEEE Access, vol. 6, pp. 26831–26842, 2018, doi: 10.1109/ACCESS.2018.2833888. https://doi.org/10.1109/ACCESS.2018.2833888
B. Li, X. Peng, Z. Wang, J. Xu, and D. Feng, “AOD-Net: All-in-One Dehazing Network,” Proc. IEEE Int. Conf. Comput. Vis., vol. 2017-Octob, pp. 4780–4788, 2017, doi: 10.1109/ICCV.2017.511. https://doi.org/10.1109/ICCV.2017.511
T. Liang, Z. Li, Y. Ren, Q. Mao, and W. Zhou, “A Progressive Single-Image Dehazing Network with Feedback Mechanism,” IEEE Access, vol. 9, pp. 158091–158097, 2021, doi: 10.1109/ACCESS.2021.3130468. https://doi.org/10.1109/ACCESS.2021.3130468
T. Wang, L. Yang, and Q. Liu, “Beetle swarm optimization algorithm: Theory and application,” Filomat, vol. 34, no. 15, pp. 5121–5137, 2020, doi: 10.2298/FIL2015121W. https://doi.org/10.2298/FIL2015121W
K. A. Kumar and R. Boda, “A Multi-Objective Randomly Updated Beetle Swarm and Multi-Verse Optimization for Brain Tumor Segmentation and Classification,” Comput. J., vol. 65, no. 4, pp. 1029–1052, 2022, doi: 10.1093/comjnl/bxab171. https://doi.org/10.1093/comjnl/bxab171
L. Zhou, K. Chen, H. Dong, S. Chi and Z. Chen, "An Improved Beetle Swarm Optimization Algorithm for the Intelligent Navigation Control of Autonomous Sailing Robots," in IEEE Access, vol. 9, pp. 5296-5311, 2021, doi: 10.1109/ACCESS.2020.3047816. https://doi.org/10.1109/ACCESS.2020.3047816
Hsieh, Cheng-Hsiung, Ze-Yu Chen, and Yi-Hung Chang. "Using Whale Optimization Algorithm and Haze Level Information in a Model-Based Image Dehazing Algorithm." Sensors 23.2 (2023): 815. https://doi.org/10.3390/s23020815
H. Zhang, V. Sindagi and V. M. Patel, "Joint Transmission Map Estimation and Dehazing Using Deep Networks," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 7, pp. 1975-1986, July 2020, doi: . https://doi.org/10.1109/TCSVT.2019.2912145
L. Li et al., "Semi-Supervised Image Dehazing," in IEEE Transactions on Image Processing, vol. 29, pp. 2766-2779, 2020, doi: 10.1109/TIP.2019.2952690. https://doi.org/10.1109/TIP.2019.2952690
P. Wang, G. Li and Y. Gao, "A Compensation Method for Random Error of Gyroscopes Based on Support Vector Machine and Beetle Antennae Search Algorithm," 2021 International Conference on Computer, Control and Robotics (ICCCR), Shanghai, China, 2021, pp. 283-287, doi: . https://doi.org/10.1109/ICCCR49711.2021.9349411
Rui Wang, Jinming Li, "A New Improved Beetle Swarm Optimization Algorithm", Proceedings of 2021 Chinese Intelligent Systems Conference, vol.805, pp.663, 2022. https://doi.org/10.1007/978-981-16-6320-8_67
Xin Cui, Cong Wang, Dongwei Ren, Yunjin Chen, Pengfei Zhu, "Semi-Supervised Image Deraining Using Knowledge Distillation", IEEE Transactions on Circuits and Systems for Video Technology, vol.32, no.12, pp.8327-8341, 2022. https://doi.org/10.1109/TCSVT.2022.3190516
Chuheng Chen, Juping Liu, Kai Chen, Mingye Ju, "Single Image Dehazing Method Based on Gradient Matrix and Ternary Parameter Search", 2021 13th International Conference on Wireless Communications and Signal Processing (WCSP), pp.1-5, 2021. https://doi.org/10.1109/WCSP52459.2021.9613598
Sameer Malik, Rajiv Soundararajan, "Semi-Supervised Learning for Low-light Image Restoration through Quality Assisted Pseudo-Labeling", 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp.4094-4103, 2023. https://doi.org/10.1109/WACV56688.2023.00409
Saradha, Dr. S., Priyanandhini, Mangayarkarasi, S., & Sreekala, T. (2019). Single Image Dehazing using Transmission Estimation. In International Journal of Innovative Technology and Exploring Engineering (Vol. 8, Issue 10, pp. 2852–2857). https://doi.org/10.35940/ijitee.j9606.0881019
John, J., & Davies, S. (2019). Image Dehazing Based on Physical Model and Gray Projection. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 1s3, pp. 241–245). https://doi.org/10.35940/ijeat.a1046.1291s319
Shrivastava, A. K., & Jain, Dr. S. (2019). A Robust Technique to Remove Fog from Single Image. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 2, pp. 3124–3130). https://doi.org/10.35940/ijrte.b2758.078219
Nagar, K., & Chawla, M. P. S. (2023). A Survey on Various Approaches for Support Vector Machine Based Engineering Applications. In International Journal of Emerging Science and Engineering (Vol. 11, Issue 11, pp. 6–11). https://doi.org/10.35940/ijese.k2555.10111123
Sistla, S. (2022). Predicting Diabetes u sing SVM Implemented by Machine Learning. In International Journal of Soft Computing and Engineering (Vol. 12, Issue 2, pp. 16–18). https://doi.org/10.35940/ijsce.b3557.0512222