A comparative Study of Image Enhancement Techniques for Natural Images
DOI:
https://doi.org/10.29304/jqcm.2022.14.4.1086Keywords:
Image Enhancement Techniques, CLAHE, DSIHE, RDSIHE, Structural Similarity Index Matrix, PSNRAbstract
Enhancement is most interesting parts in image processing field. It uses to enhance the structural appearance for picture without the degradation in the original input image. The enhancement techniques become the important key and simply extraction features by removing noise and another items inside an image. Several enhancement techniques have achieved with different and inaccurate results. The aim of this paper, the nature images quality was improved by the many enhancement techniques like of Histogram Equalization (HE), Local Histogram Equalization (LHE), Adaptive Histogram Equalization (AHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dualistic Sub-Image Histogram Equalization (DSIHE) and Recursive sub-image histogram equalization (RSIHE). In evaluation step, the performance of all these techniques are examined by values measurements of SSIM (Structural Similarity Index Matrix), Entropy, Peak Signal-to-Noise Ratio (PSNR) and Signal to Noise Ratio (SNR). The comparisons of the better existing results are given because to explain the best possible technique that can be used as suitable image enhancement. The results of the enhanced of 15 nature images have showed DSIHE technique has a batter a values of SSIM and Entropy with 0.9885 and 59975 respectively. Overall, based on the PSNR and SNR values, the CLAHE technique are recorded values higher than of other six techniques in 21.2952 and 192932 values.
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References
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[2] Agrawal, P., Chourasia, V., Kapoor, R., & Agrawal, S. (2014). A Comprehensive Study of the Image Enhancement. International Journal of Advance Foundation and Research in Computer, 1(7), 84–89.
[3] Bora, D. J. (2017). Importance of Image Enhancement Techniques in Color Image Segmentation: A Comprehensive and Comparative Study. Retrieved from http://arxiv.org/abs/1708.05081
[4] Butola, R., Pratik, S., & Kumar, U. (2015). A Comparison of Thresholding Based Image Enhancement Techniques. International Journal of Computer Science and Mobile Computing, 4(1), 314–319. Retrieved from https://www.semanticscholar.org/paper/A-Comparison-of-Thresholding-Based-Image-Techniques-Butola-Pratik/f622866172bac74d1b91eed29d0009fb67c2449a
[5] Firman Ashari, I. (2021). The Evaluation of Image Messages in MP3 Audio Steganography Using Modified Low-Bit Encoding. Telematika, 14(2), 133–145. http://doi.org/10.35671/telematika.v14i2.1031
[7] Firoz, R., Ali, M. S., Khan, M. N. U., Hossain, M. K., Islam, M. K., & Shahinuzzaman, M. (2016). Medical Image Enhancement Using Morphological Transformation. Journal of Data Analysis and Information Processing, 04(01), 1–12. http://doi.org/10.4236/jdaip.2016.41001
[8] Iqbal, S., Hussain, L., Siddiqui, G. F., Ali, M. A., Butt, F. M., & Zaib, M. (2021). Image enhancement methods on extracted texture features to detect prostate cancer by employing machine learning techniques. Waves in Random and Complex Media, 2(May 2022), 1–20. http://doi.org/10.1080/17455030.2021.1996658
[9] K.Nagaiah1, Dr. K. Manjunathachari2, D. T. V. R. (2015). Efficient Image Enhancement Techniques For Micro Calcification Detection In Mammography. International Journal of Electrical and Electronics Engineers, 1356–1363.
[10] Kaur, G., & Kaur, M. (2016). A Study of Transform Domain based Image Enhancement Techniques. International Journal of Computer Applications, 152(9), 25–29. http://doi.org/10.5120/ijca2016911858
[11] Kaur, S. (2015). Review and Analysis of Various Image Enhancement Techniques. International Journal of Computer Applications Technology and Research, 4(5), 414–418.
[12] Lee, J., Cho, S., & Kim, M. (2019). An End-to-End Joint Learning Scheme of Image Compression and Quality Enhancement with Improved Entropy Minimization, 1, 1–25. Retrieved from http://arxiv.org/abs/1912.12817
[13] Li, J., Dong, Y., & Jiao, F. (2015). An efficient sparse code fusion method for image enhancement. International Journal of Multimedia and Ubiquitous Engineering, 10(8), 55–64. http://doi.org/10.14257/ijmue.2015.10.8.06
[14] Liang, D., Li, L., Wei, M., Yang, S., Zhang, L., Yang, W., … Zhou, H. (2021). Semantically Contrastive Learning for Low-light Image Enhancement. In SixthAAAIConferenceonArtificialIntelligence (pp. 1555–1563). Retrieved from http://arxiv.org/abs/2112.06451
[15] Maithri, V., Dharshini, B. P., Vaishnavi, K., & Science, D. C. (2022). Night Time Vehicle Detection And Approximate Colour Detection Using Image Enhancement Techniques . International Research Journal of Education and Technology, 4(5), 139–146.
[16] Nayak, D. R., & Ystem, I. I. I. P. R. S. (2016). Image Enhancement Using Fuzzy Morphological Transformations. International Journal of Modern Computer Science, 4(6), 58–59.
[17] Patel, P., & Bhandari, A. (2019). A Review on Image Contrast Enhancement Techniques. Smart Moves Journal Ijoscience, 5(7), 5. http://doi.org/10.24113/ijoscience.v5i7.217
[18] Qi, Y., Yang, Z., Sun, W., Lou, M., Lian, J., Zhao, W., … Ma, Y. (2022). A Comprehensive Overview of Image Enhancement Techniques. Archives of Computational Methods in Engineering, 29(1), 583–607. http://doi.org/10.1007/s11831-021-09587-6
[19] Qureshi, M. A., Deriche, M., Beghdadi, A., & Mohandes, M. (2015). An information based framework for performance evaluation of image enhancement methods. 5th International Conference on Image Processing, Theory, Tools and Applications 2015, IPTA 2015, 2(3), 519–523. http://doi.org/10.1109/IPTA.2015.7367201
[20] Rani, A. (2014). Image Enhancement using Image Fusion Techniques. International Journal of Advanced Research in Computer Science and Software Engineering, 4(9), 413–416.
[21] Rasheed, M., Ali, A. H., Alabdali, O., Shihab, S., Rashid, A., Rashid, T., & Abed Hamad, S. H. (2021). The Effectiveness of the Finite Differences Method on Physical and Medical Images Based on a Heat Diffusion Equation. Journal of Physics: Conference Series, 1999(1). http://doi.org/10.1088/1742-6596/1999/1/012080
[22] Rasti, P., Taşmaz, H., Daneshmand, M., Kiefer, R., Ozcinar, C., & Anbarjafari, G. (2016). Satellite image enhancement: Systematic approach for denoising and resolution enhancement. Dyna (Spain), 91(3), 326–329. http://doi.org/10.6036/7676
[23] Roy, S., kumar Jain, A., Lal, S., & Kini, J. (2018). A study about color normalization methods for histopathology images. Micron, 114(August), 42–61. http://doi.org/10.1016/j.micron.2018.07.005
[24] Sankpal, S. S., & Deshpande, S. S. (2016). A review on image enhancement and color correction techniques for underwater images. Advances in Computational Sciences and Technolog, 9(1), 11–23.
[25] SatyasangramSahooa, & Dr. R. Lakshmi. (2021). Classification among Image Enhancement Techniques for Computed Tomography scan by using CancerNet neural network. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(3), 4938–4941. http://doi.org/10.17762/turcomat.v12i3.2006
[26] Shaikh, M. a, & Sayyad, S. B. (2014). Color Image Enhancement Filtering Techniques for Agricultural Domain Using Matlab. In International Symposium on “Operational Remote Sensing Applications: Opportunities, Progress and Challenges (pp. 1–5).
[27] Shaker, E., Baker, M., & Mahmood, Z. (2022). The Impact of Image Enhancement and Transfer Learning Techniques on Marine Habitat Mapping. Gazi University Journal of Science, 36(2), 1–16. http://doi.org/10.35378/gujs.973082
[28] Sharma, S., Agrawal, S., & Munjal, M. (2022). Technical Assessment of Various Image Enhancement Techniques using Finger Vein for personal Authentication. Journal of Information Technology Management, 14(2), 200–224. http://doi.org/10.22059/JITM.2022.86666
[29] Silva, D. F., Yeh, C. C. M., Zhu, Y., Batista, G. E. a. P. a., & Keogh, E. (2019). Fast Similarity Matrix Profile for Music Analysis and Exploration. IEEE Transactions on Multimedia, 21(1), 29–38. http://doi.org/10.1109/TMM.2018.2849563
[30] Singh, G., & Mittal, A. (2014). Various Image Enhancement Techniques- A Critical Review. International Journal of Innovation and Scientific Research, 10(2), 267–274.
[31] Wang, Y., Wan, R., Yang, W., Li, H., Chau, L.-P., & Kot, A. C. (2021). Low-Light Image Enhancement with Normalizing Flow. In Conference on Artificial Intelligence (pp. 1–9). Retrieved from http://arxiv.org/abs/2109.05923
[32] Yadav, S. K., Kumar, S., Kumar, B., & Gupta, R. (2017). Comparative analysis of fundus image enhancement in detection of diabetic retinopathy. In IEEE Region 10 Humanitarian Technology Conference 2016, R10-HTC 2016 - Proceedings (Vol. 7, pp. 1–5). http://doi.org/10.1109/R10-HTC.2016.7906814
[33] Yang, C., Jin, M., Jia, X., Xu, Y., & Chen, Y. (2022). AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement. IEEE, 5, 17522–17531. Retrieved from http://arxiv.org/abs/2204.13983
[34] Zhang, L., Wang, X., Dong, X., Sun, L., Cai, W., & Ning, X. (2021). Finger Vein Image Enhancement Based on Guided Tri-Gaussian Filters. ASP Transactions on Pattern Recognition and Intelligent Systems, 1(1), 17–23. http://doi.org/10.52810/tpris.2021.100012
[35] Zhang, Q., Li, M., & Deng, Y. (2018). Measure the structure similarity of nodes in complex networks based on relative entropy. Physica A: Statistical Mechanics and Its Applications, 491(October), 749–763. http://doi.org/10.1016/j.physa.2017.09.042
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Published
2022-12-02
How to Cite
Ismael, A. N. (2022). A comparative Study of Image Enhancement Techniques for Natural Images. Journal of Al-Qadisiyah for Computer Science and Mathematics, 14(4), Comp Page 53–65. https://doi.org/10.29304/jqcm.2022.14.4.1086
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