Edge Detection Quantumized: A Novel Quantum Algorithm For Image Processing

TL;DR

This paper introduces a new quantum image processing protocol that combines FRQI encoding with a modified QHED algorithm, achieving more accurate and efficient edge detection in high-resolution images.

Contribution

It presents a novel quantum edge detection method that improves accuracy and applicability over existing QHED algorithms by integrating FRQI encoding.

Findings

Enhanced edge detection accuracy over traditional QHED

Supports high-resolution image processing

Achieves faster edge detection with quantum algorithms

Abstract

Quantum image processing is a research field that explores the use of quantum computing and algorithms for image processing tasks such as image encoding and edge detection. Although classical edge detection algorithms perform reasonably well and are quite efficient, they become outright slower when it comes to large datasets with high-resolution images. Quantum computing promises to deliver a significant performance boost and breakthroughs in various sectors. Quantum Hadamard Edge Detection (QHED) algorithm, for example, works at constant time complexity, and thus detects edges much faster than any classical algorithm. However, the original QHED algorithm is designed for Quantum Probability Image Encoding (QPIE) and mainly works for binary images. This paper presents a novel protocol by combining the Flexible Representation of Quantum Images (FRQI) encoding and a modified QHED algorithm. An improved edge outline method has been proposed in this work resulting in a better object outline output and more accurate edge detection than the traditional QHED algorithm.