Image Enhancement of Palmprint Images Using High-Pass Filter and Fast Fourier Transform Methods

M. Fadly Rizky Pratama; Lailan Sofinah Harahap; Irfan Ramadani

doi:10.59934/jaiea.v5i2.2047

Authors

M. Fadly Rizky Pratama Universitas Islam Negeri Sumatera Utara
Lailan Sofinah Harahap Universitas Islam Negeri Sumatera Utara
Irfan Ramadani Universitas Islam Negeri Sumatera Utara

DOI:

https://doi.org/10.59934/jaiea.v5i2.2047

Keywords:

Palmprint recognition, , image enhancement, high-pass filtering, fast Fourier transform, quality metrics, biometric systems

Abstract

This study investigates the effectiveness of High-Pass Filter (HPF) and Fast Fourier Transform (FFT) techniques for enhancing palmprint image quality. The methodology encompasses preprocessing stages including image cropping, resizing to 256×256 pixels, grayscale conversion, and histogram equalization. Enhancement is subsequently performed using spatial-domain HPF with two coefficient variations (K=1 and K=0) and frequency-domain FFT with three distinct high-pass filters: Ideal High-Pass Filter (IHPF), Butterworth High-Pass Filter (BHPF), and Gaussian High-Pass Filter (GHPF). Experimental evaluation of 30 palmprint image samples utilizes Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) metrics. Results demonstrate that HPF with K=1 achieves superior performance with average MSE of 7.064544 dB and PSNR of 40.01314 dB. Among frequency-domain approaches, IHPF yields optimal results with average MSE of 9.354056 dB and PSNR of 38.537046 dB. The research contributes to biometric image processing through comparative analysis of spatial and frequency enhancement methods, with practical implementation via a MATLAB-based graphical interface.

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References

D. Zhang, G. Lu, and Y. Luo, Advanced Biometric Technologies: Palmprint and Hand Geometry. Springer International Publishing, 2023.

L. Wang and Y. Chen, “A comprehensive survey on palmprint recognition: Algorithms and challenges,” Biometric Syst. Rev., vol. 9, no. 1, pp. 45–67, 2024.

A. K. Jain, A. Ross, and K. Nandakumar, Introduction to Biometrics. Springer Science & Business Media, 2022.

R. C. Gonzalez and R. E. Woods, Digital Image Processing, 5th ed. Pearson Education, 2024.

M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis, and Machine Vision. Cengage Learning, 2023.

E. Prasetyo, “Digital image processing in frequency domain and restoration,” J. Inf. Technol., vol. 18, no. 4, pp. 89–102, 2022.

M. A. R. Tanjung, “Image enhancement using high-pass filter and fast {F}ourier transform methods for palmprint images,” State Islamic University of North Sumatra, Medan, Indonesia, 2021.

J. C. Russ and F. B. Neal, The Image Processing Handbook, 8th ed. CRC Press, 2023.

S. Palanikumar, M. Sasikumar, and J. Rajeesh, “Palmprint enhancement using discrete curvelet transform,” Int. J. Comput. Vis. Image Process., vol. 11, no. 3, pp. 112–125, 2021.

A. Siregar and D. W. I. Aryanta, “Simulation and analysis of digital image improvement in frequency domain using {F}ourier transform,” J. Signal Image Process., vol. 12, no. 2, pp. 77–89, 2023.