Distorted Edge Feature Extraction Using Quantum Convolutional Structure

Advancements in quantum information have significantly impacted the field of image processing, although challenges remain. In edge detection, image distortion often hinders the extraction of target boundaries. In this paper, we propose a method to extract distorted edge features by applying shallow layers in quantum convolutional neural networks (QCNN). By combining the advantages of quantum computing and the layered structure of convolutional neural networks (CNN), this approach addresses the problem and compares the extracted distorted pattern with the reference pattern, achieving a best matching ratio of 99.71 % in images interfered with impulse noise. Furthermore, we also compare the performance with the classical method and other quantum algorithms, our method gains a 97.19 % ratio.