Image Enhancement of Palmprint Images Using High-Pass Filter and Fast Fourier Transform Methods - M. Fadly Rizky Pratama; Lailan Sofinah Harahap; Irfan Ramadani (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2026)
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.