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Physica Scripta

Improved dual-threshold quantum image segmentation algorithm and simulation*

Yumin Dong, Rui Yan, Dingkang Mou, Feifei Li

Published May 24, 2024• 0 citations
Publisher

Abstract

Abstract Quantum image segmentation algorithm is crucial for quantum image processing. In this paper, a dual-threshold quantum image segmentation algorithm is designed and simulated in IBM Quantum Experience (IBM Q) platform, which can segment a complex image into three parts using fewer quantum bits. In our algorithm, given a high threshold and a low threshold, grayscale values larger than the high threshold are set to the high threshold and grayscale values smaller than the low threshold are set to the low threshold, with no change for the part between the two thresholds. Then we use a low-cost quantum comparator and design a complete and scalable quantum image segmentation circuit. Analysis of the circuit cost shows that the quantum gates required for the circuit are only related to the grayscale range q and are independent of the image size. The feasibility of the algorithm and the correctness of the quantum circuit are verified by simulation in IBM Q platform, and finally the MSE, PSNR AND SSIM value of the image is analyzed to prove the effectiveness of the segmentation algorithm.

DOI
10.1088/1402-4896/ad504d

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Abstract Quantum image segmentation algorithm is crucial for quantum image processing. In this paper, a dual-threshold quantum image segmentation algorithm is designed and simulated in IBM Quantum Experience (IBM Q) platform, which can segment a complex image into three parts using fewer quantum bits. In our algorithm, given a high threshold and a low threshold, grayscale values larger than the high threshold are set to the high threshold and grayscale values smaller than the low threshold are set to the low threshold, with no change for the part between the two thresholds. Then we use a low-cost quantum comparator and design a complete and scalable quantum image segmentation circuit. Analysis of the circuit cost shows that the quantum gates required for the circuit are only related to the grayscale range q and are independent of the image size. The feasibility of the algorithm and the correctness of the quantum circuit are verified by simulation in IBM Q platform, and finally the MSE, PSNR AND SSIM value of the image is analyzed to prove the effectiveness of the segmentation algorithm.
In order to develop and validate the quantum image processing algorithm on quantum computer, combined with quantum computing operation on IBM quantum experiment platform (IBM Q) and quantum image processing theory, a quantum image segmentation method based on IBM Q platform was designed. An improved enhanced quantum representation (IEQR) was proposed based on the existing novel enhanced quantum representation (NEQR). The quantum image segmentation circuit, which consists of the quantum bit string comparator (QBSC) and the Controlswap (Cswap) gate, is initialized according to the IEQR. Finally, quantum image segmentation of 2×2 and 4×4 size images are realized on IBM Q and local classical computer simulator. The experimental results demonstrate the feasibility and effectiveness of the algorithm and the superiority of quantum computers is validated.
The classical image segmentation algorithm based on local adaptive threshold can effectively segment images with uneven illumination, but with the increase of the image data, the real-time problem gradually emerges. In this paper, a quantum segmentation algorithm based on local adaptive threshold for NEQR image is proposed, which can use quantum mechanism to simultaneously compute local thresholds for all pixels in a gray-scale image and quickly segment the image into a binary image. In addition, several quantum circuit units, including median calculation, quantum binarization, etc. are designed in detail, and then a complete quantum circuit is designed to segment NEQR images by using fewer qubits and quantum gates. For a [Formula: see text] image with [Formula: see text] gray-scale levels, the complexity of our algorithm can be reduced to O[Formula: see text], which is an exponential speedup compared to the classic counterparts. Finally, the experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum (NISQ) era.
Abstract The intrinsic high parallelism and entanglement characteristics of quantum computing have made quantum image processing techniques a focus of great interest. One of the most widely used techniques in image processing is segmentation, which in one of their most basic forms can be carried out using thresholding algorithms. In this paper, a fault-tolerant quantum dual-threshold algorithm has been proposed. This algorithm is based on Clifford+T gates. Because T gates add fault tolerance but have the trade-off of having a much higher cost than other quantum gates,our focus has been on reducing the number of T gates. This has allowed adding noise tolerance, computational cost reduction and fault toleranceto the state-of-the-art dual-threshold segmentation circuits. Sincethe dual-threshold image segmentation involves the comparison operation,as part of this work we have implemented two comparator circuits.These circuits optimise the metrics T-count and T-depth with respect to the best circuit comparators currently available in the literature.
The classical image segmentation algorithm based on grayscale morphology can effectively segment images with uneven illumination, but with the increase of the image data, the real-time problem will emerge. In order to solve this problem, a quantum image segmentation algorithm is proposed in this paper, which can use quantum mechanism to simultaneously perform morphological operations on all pixels in a grayscale image, and then quickly segment the image into a binary image. In addition, several quantum circuit units, including dilation, erosion, bottom hat transformation, top hat transformation, etc., are designed in detail, and then they are combined together to construct the complete quantum circuits for segmenting the NEQR images. For a2n×2n2^{n} \times 2^{n}2n×2nimage withqqqgrayscale levels, the complexity of our algorithm can be reduced to O(n2+q)(n^{2}+q)(n2+q), which is an exponential speedup than the classic counterparts. Finally, the experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum (NISQ) era.
Quantum image segmentation algorithm can use its quantum mechanism to rapidly segment the objects in a quantum image. However, the existing quantum image segmentation algorithms can only segment static objects in the image and use more quantum resource(qubit). In this paper, a novel quantum segmentation algorithm based on background-difference method for NEQR image is proposed, which can segment dynamic objects in a static scene image by using fewer qubits. In addition, an efficient and feasible quantum absolute value subtractor is designed, which is an exponential improvement over the existing quantum absolute value subtractor. Then, a complete quantum circuit is designed to segment the NEQR image. For a 2n{2^n}2n×\times×2n{2^n}2n image with gray-scale range of [0,2q2^q2q-1], the complexity of our algorithm is O(qqq), which has an exponential improvement over the classical segmentation algorithm, and the complexity will not increase as the image's size increases. The experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum (NISQ) era.
Abstract The classical moving target segmentation (MTS) algorithm in a video can segment the moving targets out by calculating frame by frame, but the algorithm encounters a real-time problem as the data increases. Recently, the benefits of quantum computing in video processing have been demonstrated, but it is still scarce for MTS. In this paper, a quantum moving target segmentation algorithm for grayscale video based on background difference method is proposed, which can simultaneously model the background of all frames and perform background difference to segment the moving targets. In addition, a feasible quantum subtractor is designed to perform the background difference operation. Then, several quantum units, including quantum cyclic shift transformation, quantum background modeling, quantum background difference, and quantum binarization, are designed in detail to establish the complete quantum circuit. For a video containing 2m2^{m}2m 2 m frames (every frame is a 2n×2n2^{n} \times 2^{n}2n×2n 2 n × 2 n image with q grayscale levels), the complexity of our algorithm is O (n+q)(n+q)(n+q) ( n + q ) . This is an exponential speedup over the classical algorithm and also outperforms the existing quantum algorithms. Finally, the experiment on IBM Q demonstrates the feasibility of our algorithm in this noisy intermediate-scale quantum (NISQ) era.