Relevant bibliographies by topics / Quantum-based thresholding

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Academic literature on the topic 'Quantum-based thresholding'

Author: Grafiati
Published: 2 June 2025
Last updated: 5 July 2025

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Journal articles on the topic "Quantum-based thresholding"

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Wang, Xiangluo, Chunlei Yang, Guo-Sen Xie, and Zhonghua Liu. "Image Thresholding Segmentation on Quantum State Space." Entropy 20, no. 10 (2018): 728. http://dx.doi.org/10.3390/e20100728.

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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.
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Xu, Aidong, Wenqi Huang, Peng Li, Huajun Chen, Jiaxiao Meng, and Xiaobin Guo. "Mechanical Vibration Signal Denoising Using Quantum-Inspired Standard Deviation Based on Subband Based Gaussian Mixture Model." Shock and Vibration 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/5169070.

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Aiming at improving noise reduction effect for mechanical vibration signal, a Gaussian mixture model (SGMM) and a quantum-inspired standard deviation (QSD) are proposed and applied to the denoising method using the thresholding function in wavelet domain. Firstly, the SGMM is presented and utilized as a local distribution to approximate the wavelet coefficients distribution in each subband. Then, within Bayesian framework, the maximum a posteriori (MAP) estimator is employed to derive a thresholding function with conventional standard deviation (CSD) which is calculated by the expectation-maximization (EM) algorithm. However, the CSD has a disadvantage of ignoring the interscale dependency between wavelet coefficients. Considering this limit for the CSD, the quantum theory is adopted to analyze the interscale dependency between coefficients in adjacent subbands, and the QSD for noise-free wavelet coefficients is presented based on quantum mechanics. Next, the QSD is constituted for the CSD in the thresholding function to shrink noisy coefficients. Finally, an application in the mechanical vibration signal processing is used to illustrate the denoising technique. The experimental study shows the SGMM can model the distribution of wavelet coefficients accurately and QSD can depict interscale dependency of wavelet coefficients of true signal quite successfully. Therefore, the denoising method utilizing the SGMM and QSD performs better than others.
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Ge, Yangyang, Zhimin Wang, Wen Zheng, et al. "Optimized quantum singular value thresholding algorithm based on a hybrid quantum computer." Chinese Physics B 31, no. 4 (2022): 048704. http://dx.doi.org/10.1088/1674-1056/ac40fb.

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Quantum singular value thresholding (QSVT) algorithm, as a core module of many mathematical models, seeks the singular values of a sparse and low rank matrix exceeding a threshold and their associated singular vectors. The existing all-qubit QSVT algorithm demands lots of ancillary qubits, remaining a huge challenge for realization on near-term intermediate-scale quantum computers. In this paper, we propose a hybrid QSVT (HQSVT) algorithm utilizing both discrete variables (DVs) and continuous variables (CVs). In our algorithm, raw data vectors are encoded into a qubit system and the following data processing is fulfilled by hybrid quantum operations. Our algorithm requires O[log(MN)] qubits with O(1) qumodes and totally performs O(1) operations, which significantly reduces the space and runtime consumption.
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Zhang, Jian, Huanzhou Li, Zhangguo Tang, Qiuping Lu, Xiuqing Zheng, and Jiliu Zhou. "An Improved Quantum-Inspired Genetic Algorithm for Image Multilevel Thresholding Segmentation." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/295402.

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A multilevel thresholding algorithm for histogram-based image segmentation is presented in this paper. The proposed algorithm introduces an adaptive adjustment strategy of the rotation angle and a cooperative learning strategy into quantum genetic algorithm (called IQGA). An adaptive adjustment strategy of the quantum rotation which is introduced in this study helps improving the convergence speed, search ability, and stability. Cooperative learning enhances the search ability in the high-dimensional solution space by splitting a high-dimensional vector into several one-dimensional vectors. The experimental results demonstrate good performance of the IQGA in solving multilevel thresholding segmentation problem by compared with QGA, GA and PSO.
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Cao, Lian Lian, Sheng Ding, Xiao Wei Fu, and Li Chen. "Otsu multilevel thresholding segmentation based on quantum particle swarm optimisation algorithm." International Journal of Wireless and Mobile Computing 10, no. 3 (2016): 272. http://dx.doi.org/10.1504/ijwmc.2016.077215.

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Li, Aoqing, Fan Li, Qidi Gan, and Hongyang Ma. "Convolutional-Neural-Network-Based Hexagonal Quantum Error Correction Decoder." Applied Sciences 13, no. 17 (2023): 9689. http://dx.doi.org/10.3390/app13179689.

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Topological quantum error-correcting codes are an important tool for realizing fault-tolerant quantum computers. Heavy hexagonal coding is a new class of quantum error-correcting coding that assigns physical and auxiliary qubits to the vertices and edges of a low-degree graph. The layout of heavy hexagonal codes is particularly suitable for superconducting qubit architectures to reduce frequency conflicts and crosstalk. Although various topological code decoders have been proposed, constructing the optimal decoder remains challenging. Machine learning is an effective decoding scheme for topological codes, and in this paper, we propose a machine learning heavy hexagonal decoder based on a convolutional neural network (CNN) to obtain the decoding threshold. We test our method on heavy hexagonal codes with code distance of three, five, and seven, and increase it to five, seven, and nine by optimizing the RestNet network architecture. Our results show that the decoder thresholding accuracies are about 0.57% and 0.65%, respectively, which are about 25% higher than the conventional decoding scheme under the depolarizing noise model. The proposed decoding architecture is also applicable to other topological code families.
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Yang, Zhenlun, and Angus Wu. "A non-revisiting quantum-behaved particle swarm optimization based multilevel thresholding for image segmentation." Neural Computing and Applications 32, no. 16 (2019): 12011–31. http://dx.doi.org/10.1007/s00521-019-04210-z.

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Li, Yangyang, Xiaoyu Bai, Licheng Jiao, and Yu Xue. "Partitioned-cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation." Applied Soft Computing 56 (July 2017): 345–56. http://dx.doi.org/10.1016/j.asoc.2017.03.018.

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Pai, A. G., K. M. Buddhiraju, and S. S. Durbha. "QUANTUM INSPIRED GENETIC ALGORITHM FOR BI-LEVEL THRESHOLDING OF GRAY-SCALE IMAGES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W6-2022 (February 23, 2023): 483–88. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w6-2022-483-2023.

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Abstract. Thresholding is the primitive step in the process of image segmentation. Finding the optimal threshold for satellite images with reduced computation time and resources is still a challenging task. In this paper, we propose a Grey-Level Co-occurrence Matrix based Quantum Inspired Genetic Algorithm (QGA-GLCM) for bi-level thresholding of gray-scale images (natural and satellite). In this paper, QGA was used to find the optimal threshold. The results are compared with four different variants of Differential Evolution (DE) meta-heuristic algorithms, namely- DE-Otsu, DE-Kapur, DE-Tsali’s, DE-GLCM, and three different variants of QGA, namely- QGA-Otsu, QGA-Kapur, QGA-Tsali’s. Intensity value from image pixel is the only information used by Otsu, Tsali’s and Kapur for thresholding and are highly affected by noise. The main objective of this paper was a) To have a binary threshold for images corrupted with noise by bringing in spatial context b) To reduce the computational complexity and time for generating a threshold. Performance evaluators viz., CPU time, Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index Measure (SSIM) were used for quantitative assessment of partitioned images. From this study we observed that our proposed technique, QGA-GLCM is a) very good at producing a diverse population b) ten times faster than its classical counterparts c) generates better threshold for images corrupted by noise. In general, the threshold values generated by QGA and its variants are better than its classical counterparts. The results clearly show that exploration and exploitation capability of QGA is superior to DE for all variants. QGA-GLCM can be an effective technique to generate thresholds both in terms of computational speed and time.
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Sindugatta Nagaraja, Prajwalasimha, Naveen Kulkarani, Raghavendra M. Ichangi, et al. "QEMF for spatial domain pre-processing in iris biometrics: advancing accuracy and efficiency in recognition systems." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 1959–68. https://doi.org/10.11591/eei.v14i3.9036.

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This article presents a Quantum-Enhanced Median Filtering (QEMF) method for spatial domain pre-processing in iris biometrics, designed to improve image denoising and recognition accuracy. Traditional median filtering often struggles with high noise density, leading to inconsistencies in the denoised image. Our approach enhances the median filtering process by integrating quantum-inspired principles with statistical measures, combining median and average values of neighboring pixels. This hybrid strategy preserves the structural integrity of the original image while effectively reducing noise. Additionally, a quantum-based thresholding step is introduced in the final stage to minimize ambiguities and further enhance image quality. The proposed method is evaluated using approximately one hundred standard iris images from the Chinese University of Hong Kong (CUHK) dataset, considering four types of noise: Impulse, Poisson, Gaussian, and Speckle. Comparative analysis with conventional filters, including Median and Wiener filters, demonstrates that the QEMF method achieves 99.36% similarity to the original images, surpassing Median and Wiener filters by 1.32% and 0.34%, respectively. These results highlight the potential of quantum-enhanced filtering for improved denoising performance and increased efficiency in iris recognition systems.
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Book chapters on the topic "Quantum-based thresholding"

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Pal, Pankaj, Siddhartha Bhattacharyya, and Nishtha Agrawal. "Grayscale Image Segmentation With Quantum-Inspired Multilayer Self-Organizing Neural Network Architecture Endorsed by Context Sensitive Thresholding." In Research Anthology on Advancements in Quantum Technology. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8593-1.ch008.

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A method for grayscale image segmentation is presented using a quantum-inspired self-organizing neural network architecture by proper selection of the threshold values of the multilevel sigmoidal activation function (MUSIG). The context-sensitive threshold values in the different positions of the image are measured based on the homogeneity of the image content and used to extract the object by means of effective thresholding of the multilevel sigmoidal activation function guided by the quantum superposition principle. The neural network architecture uses fuzzy theoretic concepts to assist in the segmentation process. The authors propose a grayscale image segmentation method endorsed by context-sensitive thresholding technique. This quantum-inspired multilayer neural network is adapted with self-organization. The architecture ensures the segmentation process for the real-life images as well as synthetic images by selecting intensity parameter as the threshold value.
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Pal, Pankaj, Siddhartha Bhattacharyya, and Nishtha Agrawal. "Grayscale Image Segmentation With Quantum-Inspired Multilayer Self-Organizing Neural Network Architecture Endorsed by Context Sensitive Thresholding." In Quantum-Inspired Intelligent Systems for Multimedia Data Analysis. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5219-2.ch005.

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A method for grayscale image segmentation is presented using a quantum-inspired self-organizing neural network architecture by proper selection of the threshold values of the multilevel sigmoidal activation function (MUSIG). The context-sensitive threshold values in the different positions of the image are measured based on the homogeneity of the image content and used to extract the object by means of effective thresholding of the multilevel sigmoidal activation function guided by the quantum superposition principle. The neural network architecture uses fuzzy theoretic concepts to assist in the segmentation process. The authors propose a grayscale image segmentation method endorsed by context-sensitive thresholding technique. This quantum-inspired multilayer neural network is adapted with self-organization. The architecture ensures the segmentation process for the real-life images as well as synthetic images by selecting intensity parameter as the threshold value.
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Dey, Sandip, Siddhartha Bhattacharyya, and Ujjwal Maulik. "Chaotic Map Model-Based Interference Employed in Quantum-Inspired Genetic Algorithm to Determine the Optimum Gray Level Image Thresholding." In Global Trends in Intelligent Computing Research and Development. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4936-1.ch004.

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In this chapter, a Quantum-Inspired Genetic Algorithm (QIGA) is presented. The QIGA adopted the inherent principles of quantum computing and has been applied on three gray level test images to determine their optimal threshold values. Quantum random interference based on chaotic map models and later quantum crossover, quantum mutation, and quantum shift operation have been applied in the proposed QIGA. The basic features of quantum computing like qubit, superposition of states, coherence and decoherence, etc. help to espouse parallelism and time discreteness in QIGA. Finally, the optimum threshold value has been derived through the quantum measurement phase. In the proposed QIGA, the selected evaluation metrics are Wu’s algorithm, Renyi’s algorithm, Yen’s algorithm, Johannsen’s algorithm, Silva’s algorithm, and finally, linear index of fuzziness, and the selected gray level images are Baboon, Peppers, and Corridor. The conventional Genetic Algorithm (GA) and Quantum Evolutionary Algorithm (QEA) proposed by Han et al. have been run on the same set of images and evaluation metrics with the same parameters as QIGA. Finally, the performance analysis has been made between the proposed QIGA with the conventional GA and later with QEA proposed by Han et al., which reveals its time efficacy compared to GA along with the drawbacks in QEA.
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Dey, Sandip, Siddhartha Bhattacharyya, and Ujjwal Maulik. "Quantum Inspired Non-dominated Sorting Based Multi-objective GA for Multi-level Image Thresholding." In Series in Machine Perception and Artificial Intelligence. WORLD SCIENTIFIC, 2018. http://dx.doi.org/10.1142/9789813270237_0006.

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Conference papers on the topic "Quantum-based thresholding"

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Bhattacharyya, Siddhartha, Sandip Dey, and Debanjan Konar. "A Novel Qutrit Based Quantum Ant Colony Optimization for Multi-level Thresholding." In TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON). IEEE, 2019. http://dx.doi.org/10.1109/tencon.2019.8929561.

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Yu, HaiYan, and JiuLun Fan. "Parameter Optimization Based on Quantum Genetic Algorithm for Generalized Fuzzy Entropy Thresholding Segmentation Method." In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2008. http://dx.doi.org/10.1109/fskd.2008.454.

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Bo Lei and Jiulun Fan. "Parameter selection of generalized fuzzy entropy-based thresholding method with Quantum-Behavior Particle Swarm Optimization." In 2008 International Conference on Audio, Language and Image Processing (ICALIP). IEEE, 2008. http://dx.doi.org/10.1109/icalip.2008.4590010.

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Wang, Hong-Qi, Xin-Wen Cheng, and Guo-Chao Chen. "A Hybrid Adaptive Quantum Behaved Particle Swarm Optimization Algorithm Based Multilevel Thresholding for Image Segmentation." In 2021 IEEE International Conference on Information Communication and Software Engineering (ICICSE). IEEE, 2021. http://dx.doi.org/10.1109/icicse52190.2021.9404104.

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Mahdi, Fahad Parvez, and Syoji Kobashi. "Quantum Particle Swarm Optimization for Multilevel Thresholding-Based Image Segmentation on Dental X-Ray Images." In 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Systems (ISIS). IEEE, 2018. http://dx.doi.org/10.1109/scis-isis.2018.00181.

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Bhattacharyya, Siddhartha, and Sandip Dey. "An Efficient Quantum Inspired Genetic Algorithm with Chaotic Map Model Based Interference and Fuzzy Objective Function for Gray Level Image Thresholding." In 2011 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2011. http://dx.doi.org/10.1109/cicn.2011.24.

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Dexter, Karl J., Douglas A. Reid, and Liam P. Barry. "Nonlinear optical thresholding using a saturable absorber and two-photon absorption based device." In 11th European Quantum Electronics Conference (CLEO/EQEC). IEEE, 2009. http://dx.doi.org/10.1109/cleoe-eqec.2009.5196545.

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