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: 31 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 searchi
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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-maxi
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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
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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. Th
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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 topolo
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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,
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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.
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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 t
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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 t
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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 thres
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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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