<![CDATA[This article presents a Quantum-Enhanced Median Filtering (QEMF) method for spatial domain pre-processing in iris biometrics, designed to improve image denoising and recognition accuracy. Traditional median filtering often struggles with high noise density, leading to inconsistencies in the denoised image. Our approach enhances the median filtering process by integrating quantum-inspired principles with statistical measures, combining median and average values of neighboring pixels. This hybrid strategy preserves the structural integrity of the original image while effectively reducing noise. Additionally, a quantum-based thresholding step is introduced in the final stage to minimize ambiguities and further enhance image quality. The proposed method is evaluated using approximately one hundred standard iris images from the Chinese University of Hong Kong (CUHK) dataset, considering four types of noise: Impulse, Poisson, Gaussian, and Speckle. Comparative analysis with conventional filters, including Median and Wiener filters, demonstrates that the QEMF method achieves 99.36% similarity to the original images, surpassing Median and Wiener filters by 1.32% and 0.34%, respectively. These results highlight the potential of quantum-enhanced filtering for improved denoising performance and increased efficiency in iris recognition systems.]]>
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