A Comparison Study: Image Restoration Based on Two Heuristic Algorithms

Year 2023, Volume: 6 Issue: 1, 28 - 35, 26.07.2023

Nurşah Dincer , Kerim Dincer , Emel Arslan

Abstract

In computer science, optimization can be defined as maximizing or minimizing the result. Heuristic algorithm optimization techniques have been developed inspired by nature and to solve different optimization problems. In this study, Artificial Bee Colony (ABC) Algorithm and Firefly Algorithm (FA) have been explained in detail and a comparison between these two algorithms has been implemented. The comparison between these two algorithms is made for image restoration by using a dataset. Image restoration is the process of reducing or eliminating data loss or deterioration that may occur during the creation of an image. The loss of efficiency in the image (reducing the visual appearance of the image) is caused by noise. It is the process of obtaining the original image from the distorted image, given the knowledge of distorting factors. There are many methods applied in the literature for image restoration. In this study, two of the evolutionary algorithms have been used for this purpose and analyzed. The data set used in the study was taken from the Kaggle website. The comparison metrics are PSNR (Peak Signal-to-Noise Ratio) and MSE (Mean Squared Error). This study shows that ABC Algorithm has better results than FA on selected 20 images dataset used for blurred image restoration.

Keywords

Blurred Images, Heuristic Algorithms, Image Restoration, Artificial Bee Colony Algorithm, Firefly Algorithm

References

• Bunday, B. D. (1985). Basic Linear Programming.
• Kahaner, D., Moler, C., & Nash, S. (1989). Numerical methods and software.
• Kumar, N., Kumar, A., & Kumar, K. (2022). Color Image Contrast Enhancement Using Modified Firefly Algorithm. International Journal of Information Retrieval Research (IJIRR), 12(2), 1-18.
• Banharnsakun, A. (2022). Aerial Image Denoising Using a Best-So-Far ABC-based Adaptive Filter Method. International Journal of Computational Intelligence and Applications, 21(04), 2250024.
• Gao, Q., Gao, Y., Zhou, W., & Hua, T. (2023). Bpnn-Based Image Restoration Algorithm Optimized Using Hybrid Genetic Algorithm. SSRN.
• Sun, Y., Zhao, Z., Jiang, D., Tong, X., Tao, B., Jiang, G., ... & Fang, Z. (2022). Low-illumination image enhancement algorithm based on improved multi-scale Retinex and ABC algorithm optimization. Frontiers in Bioengineering and Biotechnology.
• Yang, J., Hu, D., & Yu, W. (2016). Artificial Bee Colony Algorithm for Image Restoration. In 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016). Atlantis Press.
• Li, C., & Chan, F. (2011). Complex-Fuzzy Adaptive Image Restoration–An Artificial-Bee-Colony-Based Learning Approach. In Intelligent Information and Database Systems: Third International Conference, ACIIDS. Springer Berlin Heidelberg.
• Kockanat, S., Karaboga, N., & Koza, T. (2012). Image denoising with 2-D FIR filter by using artificial bee colony algorithm. In 2012 International Symposium on Innovations in Intelligent Systems and Applications. IEEE.
• Sánchez-Ferreira, C., Coelho, L. S., Ayala, H. V., Farias, M. C., & Llanos, C. H. (2019). Bio-inspired optimization algorithms for real underwater image restoration. Signal Processing: Image Communication, 77, 49-65.
• Kumar, N., Dahiya, A. K., & Kumar, K. (2020). Image restoration using a fuzzy-based median filter and modified firefly optimization algorithm. Int J Adv Sci Technol, 29(4), 1471-1477.
• Savithri, K. M., & Kowsalya, G. (2016). SAR image despeckling using Bandlet transform with firefly allgorithm. International Journal of Advanced Engineering Technology.
• Csam, B. B., Tharika, F. G., Luxci, K. I., & Kumar, N. V. R. (2017). A survey on image restoration using hybrid channel based on firefly algorithm. International Conference on Information Communication and Embedded Systems (ICICES). IEEE.
• Sam, B. B., & Fred, A. L. (2019). Denoising medical images using hybrid filter with firefly algorithm. International Conference on Recent Advances in Energy-Efficient Computing and Communication (ICRAECC). IEEE.
• Bonabeau, E., Dorigo, M., Theraulaz, G., & Theraulaz, G. (1999). Swarm intelligence: from natural to artificial systems.
• https://www.kaggle.com/datasets (Date Accessed: 22.07.2022).
• Zhai, G., & Min, X. (2020). Perceptual image quality assessment: a survey. Science China Information Sciences, 63, 1-52.
• https://en.wikipedia.org/wiki/Mean_squared_error (Date Accessed: 25.07.2022).
• Hore, A., & Ziou, D. (2010). Image quality metrics: PSNR vs. SSIM. 20th international conference on pattern recognition. IEEE.
• Demoment, G. (1989). Image reconstruction and restoration: Overview of common estimation structures and problems. IEEE Transactions on Acoustics, Speech, and Signal Processing, 37(12), 2024-2036.
• Karaboğa, D. (2014). Yapay Zeka Optimizasyon Algoritmaları.
• Basti, M., & Sevkli, M. (2015). An artificial bee colony algorithm for the p-median facility location problem. International Journal of Metaheuristics, 4(1), 91-113.
• Dekhici, L., Redjem, R., Belkadi, K., & El Mhamedi, A. (2019). Discretization of the firefly algorithm for home care. Canadian Journal of Electrical and Computer Engineering, 42(1), 20-26.
• Aydilek, İ. B. (2017). Değiştirilmiş ateşböceği optimizasyon algoritması ile kural tabanlı çoklu sınıflama yapılması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 32(4), 1097-1108.
• Karaarslan, E., & Zengin, K. (2016). Ateş böceği algoritması ile haftalık ders programı hazırlama. EEB 2016 Elektrik-Elektronik ve Bilgisayar Sempozyumu, 11-13.
• Yang, X. S. (2009). Firefly algorithms for multimodal optimization. In Stochastic Algorithms: Foundations and Applications: 5th International Symposium.