Microscopic Images Improvement Depending on Dark Channel Prior and Adaptive Histogram Equalization based on the Lab Colour Model

• Autorzy: Noman Kahttan A. , Yaseen Alauldeen Salah

Identyfikatory

DOI

10.12913/22998624/188589

• Języki publikacji: EN

Abstrakty

EN

Optical microscopes face limitations due to diffraction, which can impact the clarity and resolution of the resulting images. Enhancing these images typically involves techniques such as contrast improvement, sharpening, and noise reduction, which help make features more discernible. In this study, we propose an algorithm aimed at enhancing contrast and illumination using Dark Channel Prior (DCP) and Adaptive Histogram Equalization (AHE) to improve image clarity. For illumination enhancement, we utilize the Lab color model, specifically focusing on the light formation component (L) while preserving color. This method was compared against several others, including the Retinex Algorithm with Colour Restoration, Adaptive Histogram Equalization and Fuzzy Logic, Fuzzy Logic by Stretch Membership Function, Median-Mean Based Sub-Image-Clipped Histogram Equalization, Principal Component Analysis Using Reflection Model, and Modified Color Histogram Equalization, using both reference and non-reference quality standards. Our algorithm aims to enhance image contrast and brightness without introducing color distortion, achieving favorable values for Entropy (7.913), mean of the standard deviation (61.04), Structural Similarity Index Metric (0.760), and Perception-based Image Quality Evaluator (35.324).

Słowa kluczowe

EN

DCP; image enhancement; microscope image enhancement; Lab colour space; colour restoration

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2024
• Tom: Vol. 18, no 4
• Strony: 128--136
• Opis fizyczny: Bibliogr. 20 poz., fig., tab.

Twórcy

autor

Noman Kahttan A.

• Presidency of the Council of Ministers in Iraq, Office of the Prime Minister’s Advisor for Education, Tourism and Antiquities Affairs, Baghdad, Iraq

autor

Yaseen Alauldeen Salah

• Department of Applied Sciences, University of Technology, Baghdad, Iraq

• https://orcid.org/0000-0002-2383-1398

Bibliografia

• 1. Jain A.K. Fundamentals of Digital Image Processing. Englewood Cliffs, NJ, USA: Prentice-Hall, 1989.
• 2. Daway H., Daway E., Kareem H. Colour image enhancement by fuzzy logic based on sigmoid membership function, Int. J. Intell. Eng. Syst., 2020, 13(5): 238–246.
• 3. Mohammed, M.H., Daway, H.G., Jouda, J. Automatic Cytoplasm and Nucleus detection in the white blood cells depending on hisogram analysis. In IOP
• Conference Series: Materials Science and Engineering. 2020, 871(1): 012071. IOP Publishing.
• 4. Razzak A., Hashem A. Facial expression recognition using hybrid transform, Int. J. Comput. Appl., 2015, 119(15).
• 5. Hashim A., Daway H., Kareem H. No reference Image Quality Measure for Hazy Images. Int. J. Comput. Appl., 2020, 13(6).
• 6. Daway, Esraa G., Abdulameer F.S., Daway H.G. X-ray image enhancement using retinex algorithm based on Colour restoration. J. Eng. Sci. Technol., 2022, 17(2): 1276–1286.
• 7. Zhou, M., Jin K., Wang S., Ye J., Qian D. Colour retinal image enhancement based on luminosity and contrast adjustment. IEEE Transactions on Biomedical Engineering, 2017, 65(3): 521–527.
• 8. Hsu, W.Y., Chou, C.Y. Medical image enhancement using modified Colour histogram equalization. Journal of Medical and Biological Engineering, 2015, 35: 580–584.
• 9. Singh, N., Bhandari, A.K. 2021. Principal component analysis-based low-light image enhancement using reflection model. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1–10.
• 10. Singh, K., Kapoor, R. Image enhancement via median-mean based sub-image-clipped histogram equalization. Optik, 125(17): 4646–4651. Alization. Journal of Medical and Biological Engineering, 2015, 35: 580–584.
• 11. Kadhim, Ahlam M., Daway H.G. Enhancement of Microscopy Images by Using a Hybrid Technique Based on Adaptive Histogram Equalisation and Fuzzy Logic. International Journal of Intelligent Engineering & Systems, 2023, 16(1).
• 12. Zuiderveld, K. Contrast Limited Adaptive Histograph Equalization. Graphic Gems IV. San Diego: Academic Press Professional, 1994, 474–485.
• 13. He K., Sun J., Tang X. Single image haze removal using dark channel prior, IEEE Trans. Pattern Anal. Mach. Intell., 2010, 33(12): 2341–2353.
• 14. Ebner M. Colour constancy, John Wiley and Sons, 2007, 7.
• 15. Reinhard E., Khan E., Akyuz A., Johnson G. Colour imaging: fundamentals and applications, CRC Press, 2008.
• 16. Gonzalez R.C., Woods R.E., Eddins S.L. Digital Image Processing Using MATLAB. New Jersey, Prentice Hall, 2003.
• 17. Hashim A.R., Daway H.G., Kareem H.H. Single image dehazing by dark channel prior and luminance adjustment, Imaging Sci. J., 2020, 68(5–8): 278–287.
• 18. Zhou W., Bovik A.C., Sheikh H.R., Simoncelli E.P. Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing, 2004, 13(4): 600–612.
• 19. Venkatanath N., Praneeth D., Bh, M.C., Channappayya S.S., Medasani S.S. 2015. blind image quality evaluation using perception based features. in 2015 twenty first national conference on communications.
• 20. https://www.kaggle.com/datasets/gangadhar/nuclei-segmentation-in-microscope-cell-images.