A NEW HISTOGRAM EQUALIZATION METHOD WITH MODIFIED DISCRETE HAAR WAVELET TRANSFORM

Abstract

Histogram equalization is one of the methods used to increase the contrast of digital images. In the ideal histogram equalization methods, the contrast are stretched by preserving the image similarity between input and output images, making the distributions in the histogram uniform. In this study conducted in the frequency domain, a new image contrast stretching method with Haar Wavelet Transform (HWT) in Low Dynamic Range is proposed. In this method, using the Probability Mass Function (PMF), a suppression process is applied to the noisy frequencies in the high pass channel of the frequencies. Subsequent frequency transformations provide a feasible reduction in the dynamic range of histogram frequencies. This process in the frequency domain improves the image quality by increasing the standard deviation in the image. In experimental studies over a benchmarking dataset, the proposed method is compared with conventional methods and promising results are obtained. In the experimental studies, Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Structural Similarity Index Measurement (SSIM) and Contrast value, which are among the image quality evaluation metrics, are used. In this proposed approach, image quality is evaluated both qualitatively and quantitatively assessments, and successful results are obtained.

Keywords

• Low Dynamic Range
• Haar Wavelet Transform
• Histogram Equalization
• Contrast Enhancement

DEĞİŞTİRİLMİŞ AYRIK HAAR DALGACIK DÖNÜŞÜMÜ İLE YENİ BİR HİSTOGRAM EŞİTLEME YÖNTEMİ

Abstract

Histogram eşitleme dijital görüntülerin kontrastını artırmak için kullanılan yöntemlerden biridir. İdeal histogram eşitleme yöntemlerinde, girdi ve çıktı arasındaki görüntü benzerliğini koruyarak histogramdaki dağılımları tekdüze hale getirerek kontrast gerilmektedir. Frekans alanında yapılan bu çalışmada, Düşük Dinamik Aralığında değiştirilmiş ayrık Haar Dalgacık Dönüşümü ile yeni bir görüntü kontrast germe yöntemi önerilmiştir. Bu yöntemde Olasılık Kütle Fonksiyonunu ile frekansların yüksek geçiş kanalında gürültülü frekanslara bir baskılama işlemi gerçekleştirilmiştir. Daha sonra yapılan frekans dönüşümlerinde histogram frekansların dinamik aralıklarında önemli bir azalma sağlanmıştır. Frekans alanındaki bu işlem görüntüde standart sapmanın artmasını sağlayarak görüntü kalitesinin iyileşmesini sağlar. Kıyaslamalı bir veri seti üzerinde yapılan deneysel çalışmalarda, önerilen yöntem konvansiyonel metotlarla kıyaslanmış ve umut verici sonuçlar elde edilmiştir. Görüntü kalitesi değerlendirme metriklerinden Tepe Sinyal Gürültü Oranı (PSNR), Ortalama Kare Hata (MSE), Yapısal Benzerlik Endeks Ölçütü (SSIM) ve Kontrast değeri deneysel çalışmalarda kullanılmıştır. Önerilen bu yaklaşım ile elde edilen sonuçlar diğer algoritmaların sonuçları ile kıyaslandığında hem kalitatif hem de kantitatif açıdan başarılı bulunmuştur.

Keywords

• Düşük Dinamik Aralığı
• Haar Dalgacık Dönüşümü
• Histogram Eşitleme
• Kontrastı İyileştirme

References

1. Tung, T. C., & Fuh, C. S. (2021). ICEBIN: Image Contrast Enhancement Based on Induced Norm and Local Patch Approaches. IEEE Access, 9, 23737-23750.
2. Lecca, M., Rizzi, A., & Serapioni, R. P. (2021). An Image Contrast Measure Based on Retinex Principles. IEEE Transactions on Image Processing, 30, 3543-3554.
3. Tung, T. C., & Fuh, C. S. (2021). ICEBIN: Image Contrast Enhancement Based on Induced Norm and Local Patch Approaches. IEEE Access, 9, 23737-23750.
4. Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing (4th Edition), Pearson Publishing, March 2017.
5. Shaik, Ahmad, and V. Thanikaiselvan. "Comparative Analysis of Integer Wavelet Transforms in Reversible Data Hiding Using Threshold Based Histogram Modification." Journal of King Saud University-Computer and Information Sciences, 2018.
6. Shih-Chia Huang, Chien-Hui Yeh, Image Contrast enhancement for preserving mean brightness without losing image features, In Engineering Applications of Artificial Intelligence, Volume 26, Issues 5–6, 2013, Pages 1487-1492
7. Dongwook Cho, Tien D. Bui, Fast image enhancement in compressed wavelet domain, In Signal Processing, Volume 98, 2014, Pages 295-307
8. Muhammad Zafar Iqbal, Abdul Ghafoor, Adil Masood Siddiqui, Muhammad Mohsin Riaz, Umar Khalid, Dual-tree complex wavelet transform and SVD based medical image resolution enhancement, In Signal Processing, Volume 105, 2014, Pages 430-437

9. Lidong, Huang, et al. Combination of Contrast limited adaptive histogram equalisation and discrete wavelet transform for image enhancement. IET Image Processing, 2015, 9.10: 908-915.
10. Daniel, Ebenezer; Anitha, J. Optimum wavelet based masking for the Contrast enhancement of medical images using enhanced cuckoo search algorithm. Computers in biology and medicine, 2016, 71: 149-155.
11. Kim, Se Eun; Jeon, Jong Ju; Eom, Kyu. Image Contrast enhancement using entropy scaling in wavelet domain. Signal Processing, 2016, 127: 1-11.
12. Jenifer, Sheeba; Parasuraman, S.; Kadirvelu, Amudha. Contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped Contrast-limited adaptive histogram equalization algorithm. Applied Soft Computing, 2016, 42: 167-177.
13. Nithyananda, C. R., et al. Survey on Histogram Equalization method based Image Enhancement techniques. In: Data Mining and Advanced Computing (SAPIENCE), International Conference on. IEEE, 2016. p. 150-158.
14. Kaur, Amandeep; Singh, Chandan. Contrast enhancement for cephalometric images using wavelet-based modified adaptive histogram equalization. Applied Soft Computing, 2017, 51: 180-191.
15. Liu, Yun-Fu; Guo, Jing-Ming; Yu, Jie-Cyun. Contrast Enhancement using Stratified Parametric-Oriented Histogram Equalization. IEEE Transactions on Circuits and Systems for Video Technology, 2016.
16. Yelmanova, Elena; Romanyshyn, Yuriy. Histogram-based method for image Contrast enhancement. In: Experience of Designing and Application of CAD Systems in Microelectronics (CADSM), 2017 14th International Conference The. IEEE, 2017. p. 165-169.
17. Bharadi, Vinayak Ashok; Padole, Latika. Performance comparison of hybrid wavelet transform-I variants and Contrast limited adaptive histogram equalization combination for image enhancement. In: Wireless and Optical Communications Networks (WOCN), Fourteenth International Conference on. IEEE, 2017. p. 1-8.
18. Bharadi, Vinayak Ashok; Padole, Latika. Hybrid wavelet transform I and II combined with Contrast limited adaptive histogram equalization for image enhancement. In: Wireless and Optical Communications Networks (WOCN), 2017 Fourteenth International Conference on. IEEE, 2017. p. 1-7.
19. Y. Wang, Q. Chen, and B. Zhang, “Image enhancement based on equal area dualistic sub-image histogram equalization method,” in IEEE Transactions on Consumer Electronics, vol. 45, pp. 68–75, Feb 1999.
20. Y.-T. Kim, “Contrast enhancement using brightness preserving bi histogram equalization,” in IEEE Transactions on Consumer Electronics, vol. 43, pp. 1–8, Feb 1997.
21. S.-D.Chenand A.Ramli,“Minimum mean brightness error bi-histogram equalization in Contrast enhancement,” in IEEE Transactions on Consumer Electronics, vol. 49, pp. 1310–1319, Nov 2003.
22. Malik, R., Pande, S., Khamparia, A., & Bhushan, B. (2021). 3 Contrast enhancement approach for satellite images using hybrid fusion technique and artificial bee colony optimization. In Nature-Inspired Optimization Algorithms (pp. 33-54). De Gruyter.
23. Fujioka, T., Yashima, Y., Oyama, J., Mori, M., Kubota, K., Katsuta, L., ... & Tateishi, U. (2021). Deep-learning approach with convolutional neural network for classification of maximum intensity projections of dynamic Contrast-enhanced breast magnetic resonance imaging. Magnetic Resonance Imaging, 75, 1-8.
24. Das, P., & Das, A. (2020, December). Adaptive Gabor Filtering using Grey Wolf Optimization for Enhancement of Brain MRI. In 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE) (pp. 356-359). IEEE.
25. Luque-Chang, A., Cuevas, E., Pérez-Cisneros, M., Fausto, F., Valdivia-González, A., & Sarkar, R. (2021). Moth swarm algorithm for image Contrast enhancement. Knowledge-Based Systems, 212, 106607.
26. Aurangzeb, K., Aslam, S., Alhussein, M., Naqvi, R. A., Arsalan, M., & Haider, S. I. (2021). Contrast Enhancement of Fundus Images by Employing Modified PSO for Improving the Performance of Deep Learning Models. IEEE Access, 9, 47930-47945.
27. Alenezi, F., & Santosh, K. C. (2021). Geometric Regularized Hopfield Neural Network for Medical Image Enhancement. International Journal of Biomedical Imaging, 2021.
28. Jose, J., Gautam, N., Tiwari, M., Tiwari, T., Suresh, A., Sundararaj, V., & Rejeesh, M. R. (2021). An image quality enhancement scheme employing adolescent identity search algorithm in the NSST domain for multimodal medical image fusion. Biomedical Signal Processing and Control, 66, 102480.
29. Janan, F., & Brady, M. (2021). RICE: A method for quantitative mammographic image enhancement. Medical image analysis, 102043.
30. Spille, D. C., Adeli, A., Sporns, P. B., Heß, K., Streckert, E. M. S., Brokinkel, C., ... & Brokinkel, B. (2021). Predicting the risk of postoperative recurrence and high-grade histology in patients with intracranial meningiomas using routine preoperative MRI. Neurosurgical review, 44(2), 1109-1117.
31. Brown, J., Somo, S., Brooks, F., Komarov, S., Zhou, W., Anastasio, M., & Brey, E. (2020). X-ray CT in phase contrast enhancement geometry of alginate microbeads in a whole-animal model. Annals of biomedical engineering, 48(3), 1016-1024.
32. Belaid, N., Adjabi, S., Zougab, N. and Kokonendji, C.C., “Bayesian bandwidth selection in discrete multivariate associated kernel estimators for probability mass functions”, Journal of the Korean Statistical Society, 45(4), pp.557-567, 2016.
33. Larson, Eric C.; Chandler, Damon M. Most apparent distortion: full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging, 2010, 19.1: 011006-011006-21.
34. Basha, M.S., Kumar, G.K. and Subbaiah, B.R., “Image Quality Measurements: A Review”, International Journal of Innovations & Advancement in Computer Science, Vol. 6, No:12, pp. 306-310, 2017.
35. Jang, C.Y., Kang, S.J. and Kim, Y.H., 2016. Adaptive Contrast enhancement using edge-based lighting condition estimation. Digital Signal Processing, 58, pp.1-9.
36. Sahu, Sima, Amit Kumar Singh, S. P. Ghrera, and Mohamed Elhoseny. "An approach for de-noising and Contrast enhancement of retinal fundus image using CLAHE." Optics & Laser Technology 110 (2019) ): 87-98.