Training-free Quantum-Inspired Image Edge Extraction Method

TL;DR

This paper introduces a training-free, quantum-inspired edge detection method that combines classical and quantum concepts to improve accuracy and robustness in complex, noisy images without requiring extensive training.

Contribution

The paper presents a novel training-free edge detection approach integrating classical Sobel, Schrödinger wave equation refinement, and hybrid Canny-Laplacian frameworks, enhancing robustness and accuracy.

Findings

Achieves state-of-the-art metrics on multiple datasets.

Demonstrates high robustness under noisy conditions.

Outperforms traditional methods in complex scenarios.

Abstract

Edge detection is a cornerstone of image processing, yet existing methods often face critical limitations. Traditional deep learning edge detection methods require extensive training datasets and fine-tuning, while classical techniques often fail in complex or noisy scenarios, limiting their real-world applicability. To address these limitations, we propose a training-free, quantum-inspired edge detection model. Our approach integrates classical Sobel edge detection, the Schr\"odinger wave equation refinement, and a hybrid framework combining Canny and Laplacian operators. By eliminating the need for training, the model is lightweight and adaptable to diverse applications. The Schr\"odinger wave equation refines gradient-based edge maps through iterative diffusion, significantly enhancing edge precision. The hybrid framework further strengthens the model by synergistically combining local and global features, ensuring robustness even under challenging conditions. Extensive evaluations on datasets like BIPED, Multicue, and NYUD demonstrate superior performance of the proposed model, achieving state-of-the-art metrics, including ODS, OIS, AP, and F-measure. Noise robustness experiments highlight its reliability, showcasing its practicality for real-world scenarios. Due to its versatile and adaptable nature, our model is well-suited for applications such as medical imaging, autonomous systems, and environmental monitoring, setting a new benchmark for edge detection.