| Sumario: | Iris recognition, renowned for its exceptional precision, has been extensively utilized across diverse industries. However, the presence of noise and blur frequently compromises the quality of iris images, thereby adversely affecting recognition accuracy. In this research, we have refined the traditional Wiener filter image restoration technique by integrating it with a gradient descent strategy, specifically employing the Barzilai-Borwein (BB) step size selection. This innovative approach is designed to enhance both the precision and resilience of iris recognition systems. The BB gradient method is adept at optimizing the parameters of the Wiener filter by introducing simulated blurring and noise conditions to the iris images. Through this process, it is capable of restoring images that have been degraded by blur and noise, leading to a significant improvement in the clarity of the restored images and, consequently, a notable elevation in recognition performance. The results of our experiments have demonstrated that this advanced method surpasses conventional filtering techniques in terms of both subjective visual quality assessments and objective peak signal-to-noise ratio (PSNR) evaluations.
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