AI-BASED EAR IMAGE QUALITY IMPROVEMENT ALGORITHM
Keywords:
earlobe image, biometric identification, computer vision, digital image processing, deep learning, convolutional neural networks, generative models (GAN), image enhancement, denoising, super-resolution, contrast enhancement, feature extraction, PSNR, SSIM, artificial intelligence algorithms.Abstract
This article analyzes modern algorithms aimed at improving the quality of ear images based on artificial intelligence. The research uses computer vision and digital image processing methods, which are one of the important areas of image processing. In particular, convolutional neural networks (CNN), deep learning, and generative models are used to restore and improve low-quality, noisy, or low-resolution ear images.
During the work, stages such as image denoising, super-resolution, contrast enhancement, and contour detection are performed. The proposed algorithm serves to increase the accuracy of ear biometric systems, make the identification process reliable, and improve the efficiency of working with images in real conditions. Experimental results show that artificial intelligence-based approaches have higher accuracy and quality indicators than traditional methods.
The results of this research can be widely used in areas such as biometric identification, security systems, and medical image analysis.
