Discrete Wavelet Transform and Event-triggered Particle Swarm Optimization Approach for Infrared Image Enhancement

G. Onuh¹ , J.B. Akan² , B. Muhammad³ , R.I. Nwosu⁴

Section:Research Paper, Product Type: Journal-Paper
Vol.8 , Issue.4 , pp.83-89, Aug-2020

Online published on Aug 31, 2020

Copyright © G. Onuh, J.B. Akan, B. Muhammad, R.I. Nwosu . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Infrared images suffer from low contrast and poor image quality mainly as a result of data collection and transmission. Conventional techniques used in infrared image enhancement have being reported with over enhancement and brightness distortion. This work proposes an infrared image enhancement technique based on discrete wavelet transform (DWT) and event-triggered particle swarm optimization (ETPSO). This technique will be implemented by first performing image preprocessing using Daubechies D4 filter, image transformation and enhancement based on discrete wavelet transform and finally brightness correction using event-triggered particle swarm optimization. The proposed algorithm was implemented on a dataset obtained from Dynamic Graphics Project laboratory database of infrared images and the output was put side by side with conventional approaches. A quantitative comparison shows that the proposed technique performs better with an average peak signal-to-noise ratio (PSNR) and discrete entropy (DE) values of 20.9 and 6.49 respectively

Key-Words / Index Term :
Infrared Image Enhancement; Discrete Wavelet Transform; Particle Swarm Optimization

References :
[1] B. Dehda and K. Melkemi, ?Image denoising using new wavelet thresholding function,? J. Appl. Math. Comput. Mech., vol. 16, no. 2, pp. 55?65, 2017, doi: 10.17512/jamcm.2017.2.05.
[2] M. Jiang, ?Edge enhancement and noise suppression for infrared image based on feature analysis,? Infrared Phys. Technol., vol. 91, no. April, pp. 142?152, 2018, doi: 10.1016/j.infrared.2018.04.005.
[3] M. Wan, G. Gu, W. Qian, K. Ren, Q. Chen, and X. Maldague, ?Infrared image enhancement using adaptive histogram partition and brightness correction,? Remote Sens., vol. 10, no. 5, 2018, doi: 10.3390/rs10050682.
[4] V. Janani and M. Dinakaran, ?Infrared image enhancement techniques - A review,? 2nd Int. Conf. Curr. Trends Eng. Technol. ICCTET 2014, pp. 167?173, 2014, doi: 10.1109/ICCTET.2014.6966282.
[5] L. Liu, L. Xu, and H. Fang, ?Infrared and visible image fusion and denoising via norm minimization,? Signal Processing, p. 107546, 2020, doi: 10.1016/j.sigpro.2020.107546.
[6] S. Budzan and R. Wyżgolik, ?Remarks on noise removal in infrared images,? Meas. Autom. Monit., vol. 61, no. 6, pp. 187?190, 2015.
[7] C. Gao, ?Infrared Image Enhancement Method Based on Discrete stationary Wavelet Transform and CLAHE,? 2019 IEEE Int. Conf. Comput. Sci. Educ. Informatiz., pp. 191?194, 2019.
[8] V. Voronin, S. Tokareva, E. Semenishchev, and S. Agaian, ?Thermal image enhancement algorithm using local and global logarithmic transform histogram matching with spatial equalization,? Proc. IEEE Southwest Symp. Image Anal. Interpret., vol. 2018?April, pp. 5?8, 2018, doi: 10.1109/SSIAI.2018.8470344.
[9] J. Huang, Y. Ma, Y. Zhang, and F. Fan, ?Infrared image enhancement algorithm based on adaptive histogram segmentation,? Appl. Opt., vol. 56, no. 35, p. 9686, 2017, doi: 10.1364/ao.56.009686.
[10] B. Wang, L. L. Chen, and Y. Z. Liu, ?New results on contrast enhancement for infrared images,? Optik (Stuttg)., vol. 178, pp. 1264?1269, 2019, doi: 10.1016/j.ijleo.2018.09.165.
[11] V. E. Vickers, ?Plateau equalization algorithm for real‐time display of high‐quality infrared imagery,? Opt. Eng., vol. 35, no. 7, p. 1921, 1996, doi: 10.1117/1.601006.
[12] S. Li, W. Jin, L. Li, and Y. Li, ?An improved contrast enhancement algorithm for infrared images based on adaptive double plateaus histogram equalization,? Infrared Phys. Technol., vol. 90, pp. 164?174, 2018, doi: 10.1016/j.infrared.2018.03.010.
[13] S. Der Chen and A. R. Ramli, ?Minimum mean brightness error bi-histogram equalization in contrast enhancement,? IEEE Trans. Consum. Electron., vol. 49, no. 4, pp. 1310?1319, 2003, doi: 10.1109/TCE.2003.1261234.
[14] C. Zuo, Q. Chen, and X. Sui, ?Range Limited Bi-Histogram Equalization for image contrast enhancement,? Optik (Stuttg)., vol. 124, no. 5, pp. 425?431, 2013, doi: 10.1016/j.ijleo.2011.12.057.
[15] F. Fan, Y. Ma, J. Huang, and Z. Liu, ?Infrared image enhancement based on saliency weight with adaptive threshold,? 2018 IEEE 3rd Int. Conf. Signal Image Process. ICSIP 2018, pp. 225?230, 2019, doi: 10.1109/SIPROCESS.2018.8600468.
[16] I. I. Database, ?No Title,? Digital Graphics Project Laboratory, 2007. [Online]. Available: https://www.dgp.toronto.edu/~nmorris/data/IRData. [Accessed: 02-Jun-2019].
[17] M. S. Sohail, M. O. Bin Saeed, S. Z. Rizvi, M. Shoaib, and A. U. H. Sheikh, ?Low-Complexity Particle Swarm Optimization for Time-Critical Applications,? pp. 1?24, 2014.
[18] R. D. Pai, P. Srinivashalvi, and P. Basavarajhiremath, ?Medical color image enhancement using wavelet transform and contrast stretching technique Medical Color Image Enhancement using Wavelet Transform and Contrast Stretching Technique,? vol. 5, no. October, pp. 0?7, 2015.
[19] M. Wan, G. Gu, W. Qian, K. Ren, Q. Chen, and X. Maldague, ?Particle swarm optimization-based local entropy weighted histogram equalization for infrared image enhancement,? Infrared Phys. Technol., vol. 91, pp. 164?181, 2018, doi: 10.1016/j.infrared.2018.04.003.