Quantum image edge detection based on Haar wavelet transform

Quantum edge detection offers a promising avenue for real-time image analysis, addressing constraints faced by classical algorithms. However, existing quantum edge detection methods often rely on classical edge detection operators, leading to the loss of intricate edge details, especially in high-resolution images. Here, we present a novel quantum image edge detection algorithm. Our approach involves transforming the image into the wavelet domain through wavelet transform, performing edge detection, and obtaining the edge image via inverse wavelet transform. This innovative method not only mitigates edge information loss but also enhances precision in delineation. Through comprehensive simulations on a classical computing platform, employing peak signal-to-noise ratio (PSNR) and Edge Preservation Index (EPI) evaluations, our proposed scheme demonstrates superior edge information and heightened accuracy. These results underscore the potential of our approach in advancing image processing techniques.