Edge Detection Quantumized: A Novel Quantum Algorithm For Image Processing

TL;DR

This work addresses the scalability and accuracy limits of Quantum Hadamard Edge Detection (QHED) by replacing the QPIE-based encoding with Flexible Representation of Quantum Images (FRQI) and introducing a modified edge-detection protocol. By encoding images via FRQI, applying partial measurement on an ancilla, and augmenting QHED with an extra qubit and thresholded post-processing, the approach improves edge outlines and reduces noise for complex images. The authors also discuss a quantum thermodynamics perspective, highlighting information loss vs edge preservation during partial measurements. Overall, the protocol broadens the applicability of quantum edge detection and provides a physics-informed pathway toward more robust quantum image processing in practice.

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.

Figures (9)

• Figure 1: System Diagram of the Proposed Model
• Figure 2: Comparison between Traditional QHED and Proposed Model.
• Figure 3: FRQI Encoding circuit for a $2 \times 2$ sample color image.
• Figure 4: Quantum Circuit of QHED Algorithm.
• Figure 5: (a) image $-1$, (b) image $-2$. We note that the map doesn't change the edges.