(QPIE:QHED) Edge Detection Using Optimal Feature Subset Selection Based on Detection of for Quantum Image Processing

Abstract

 Image processing and detection are natural extensions of detecting the edges, curves, and areas of sudden changes in brightness or, more specifically, missing pixels. Edge detection is a set of mathematical algorithms. Change detection is a term used to describe the process of identifying discrete changes in single-digit data. The dimension. The ability to detect and remove image edges is fundamental to image processing and computer and machine vision. One way to detect and remove features from images is to use edge detection. In this work, we first provide an overview of research in this area, and then list some of the concerns that have been raised regarding rapid development and its achievement. We see “quantum image classification and recognition” as the ideal application to showcase the potential of quantum technology. Quantum Probabilistic Image Encoding (QPIE:QHED) was used in our study to transform classical data into quantum states. It is necessary to apply quantum algorithms to classical problems, a method for quantum Hadamard edge detection, and also uses them to process quantum image data, often produced by QPIE, using QHED, a modern edge detection technique. Quite simply, QHED is a technology for precise edge detection in images that uses quantum computing.