Image Thresholding Segmentation on Quantum State Space

Author:

Wang Xiangluo,Yang Chunlei,Xie Guo-Sen,Liu Zhonghua

Abstract

Aiming to implement image segmentation precisely and efficiently, we exploit new ways to encode images and achieve the optimal thresholding on quantum state space. Firstly, the state vector and density matrix are adopted for the representation of pixel intensities and their probability distribution, respectively. Then, the method based on global quantum entropy maximization (GQEM) is proposed, which has an equivalent object function to Otsu’s, but gives a more explicit physical interpretation of image thresholding in the language of quantum mechanics. To reduce the time consumption for searching for optimal thresholds, the method of quantum lossy-encoding-based entropy maximization (QLEEM) is presented, in which the eigenvalues of density matrices can give direct clues for thresholding, and then, the process of optimal searching can be avoided. Meanwhile, the QLEEM algorithm achieves two additional effects: (1) the upper bound of the thresholding level can be implicitly determined according to the eigenvalues; and (2) the proposed approaches ensure that the local information in images is retained as much as possible, and simultaneously, the inter-class separability is maximized in the segmented images. Both of them contribute to the structural characteristics of images, which the human visual system is highly adapted to extract. Experimental results show that the proposed methods are able to achieve a competitive quality of thresholding and the fastest computation speed compared with the state-of-the-art methods.

Publisher

MDPI AG

Subject

General Physics and Astronomy

Cited by 11 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Quantum Image Processing Using Edge Detection Based on Roberts Cross Operators;Lecture Notes in Networks and Systems;2024

2. A Novel Image Segmentation Algorithm based on Continuous-Time Quantum Walk using Superpixels;International Journal of Theoretical Physics;2023-12-30

3. Medical image edge detection in the framework of quantum representations;Alexandria Engineering Journal;2023-10

4. A Fast Quantum Image Component Labeling Algorithm;Mathematics;2022-08-01

5. Review of Quantum Image Processing;Archives of Computational Methods in Engineering;2021-05-14

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