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IETE Journal of Research

Quantum Image Edge Extraction Algorithm for Noisy Image

Rajib Chetia, Partha Pratim Sahu

Published Aug 24, 2023• 6 citations
Publisher

Abstract

Image preprocessing is important for noise exclusion in order to improve the accuracy of contour detection and image edge extraction. In this paper, a dual quantum spatial filter is proposed in order to reduce statistical noise, image blurring, impulsive noise, and retained details. Non-Maximum-Suppression and instead of manually operating threshold, adaptive thresholding operation is used in the quantum edge detection algorithm to detect more edge information with sharp edges. The circuit of the algorithm is realized through a series of operatives. We have compared PSNR, SSIM, precision rate, accuracy rate, recall rate, error rate, and computational complexity of the algorithm with operator based some existing classical and quantum edge extraction algorithms. The algorithm has the potential to get improved edge continuity, greater sharpness, and extract edge information having less proportion of noise.

DOI
10.1080/03772063.2023.2248950

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Image preprocessing is important for noise exclusion in order to improve the accuracy of contour detection and image edge extraction. In this paper, a dual quantum spatial filter is proposed in order to reduce statistical noise, image blurring, impulsive noise, and retained details. Non-Maximum-Suppression and instead of manually operating threshold, adaptive thresholding operation is used in the quantum edge detection algorithm to detect more edge information with sharp edges. The circuit of the algorithm is realized through a series of operatives. We have compared PSNR, SSIM, precision rate, accuracy rate, recall rate, error rate, and computational complexity of the algorithm with operator based some existing classical and quantum edge extraction algorithms. The algorithm has the potential to get improved edge continuity, greater sharpness, and extract edge information having less proportion of noise.
Quantum image processing is a system where both quantum computing and image processings are key. Now it is required to investigate how to apply image processing concepts such as image edge detection and improved Sobel operator, to quantum image processing. Just as traditional image processing helps to image manipulation, our efforts in quantum image processing contribute to the further development of quantum algorithm and quantum information theory. Edge detection is an important problem in traditional image processing, and image edge detection algorithm can filter image edge features and retain important attributes. We proposed a quantum edge detection algorithm, which employs improved Sobel operator to improve the performance, so as to solve the problem of unsatisfactory traditional image edge detection methods. In practical application, the amount of image data to be processed increases sharply, and the computing power of classical computer becomes a limitation. Quantum information processing can effectively accelerate many classical problems by virtue of quantum mechanical characteristics, such as quantum superposition, entanglement, parallelism. Our proposed method uses the novel enhanced quantum representation to store quantum images, which stores all the pixels in the image in a superimposed state, realizing parallel computation. The improved eight-direction Sobel operator is used to calculate the gray gradient, and the quantum circuit is designed to realize quantum edge detection. Simulation results have shown that our algorithm can extract edge with dimension 2n× 2nwhen the computational complexity is O(n2+ 2q+3). The improved algorithm can detect more edge details and has strong adaptability.
Quantum edge detection is one of the important part of quantum image processing. In this paper, a quantum edge detection algorithm is designed for the quantum representation of multi-wavelength image (QRMW) model. The algorithm includes all stages of filtering, enhancement and detection. The proposed algorithm is also designed to apply any filtering operation to QRMW images, not only for a particular filtering operation. The proposed algorithm aims to solve the problems that quantum edge detection algorithms in the literature have processing only for a particular operator and noise reduction. Moreover, the algorithm aims to perform operations more efficiently by using less resources. Low-pass filter (LPF) smoothing operators are applied in the filtering stage for the noise reduction problem. In order to apply all filtering operations to the image, arithmetic operators that can operate with all signed integers are used in the algorithm. The operators Sobel, Prewitt and Scharr in the enhancement stage and the gradient method in the detection stage are used for both verification of the proposed algorithm and comparisons with the existing algorithms. A method with quantitative outcomes is shown to evaluate the performance of the edge detection algorithms. Analysis of the simulations performed on sample images with different operators. The circuit complexity of the algorithm is presented and the comparisons are made with the existing studies. The superiority of the proposed algorithm and its flexibility to be used in other studies are clearly demonstrated by analysis.
For some image processing algorithms such as edge detection and segmentation, filtering in the spatial domain is an important pre-processing to improve the quality of the image by removing the noise in the images. The time and memory requirements for processing also increase as the size of the images increases. Besides, excessive blurring of the image during noise removal operation can lead to excessive deterioration of image quality. Therefore, the balance between noise reduction and blur needs to be well adjusted. In this paper, a new quantum method for noise reduction on quantum images using QFT-based arithmetic operators is proposed and quantum circuits are designed. Mean filtering operators of different sizes are used to remove noise. First, the classical image is represented by the gray scale quantum image model, the novel enhanced quantum representation (NEQR) model. A quantum method is proposed and circuits are presented for the realization of Mean filters of different sizes on the basis of addition and division operations. Finally, the performance of the method is evaluated by presenting the circuit complexity of the method and the experimental results. In the proposed method, it is aimed to use less resources and reduce the circuit complexity. The optimal filtering operator size is investigated for the balance between noise reduction and image blur.
Identification of image edges using edge detection is done to obtain images that are sharp and clear. The selection of the edge detection algorithm will affect the result. Canny operators have an advantage compared to other edge detection operators because of their ability to detect not only strong edges but also weak edges. Until now, Canny edge detection has been done using classical computing where data are expressed in bits, 0 or 1. This paper proposes the identification of image edges using a quantum Canny edge detection algorithm, where data are expressed in the form of quantum bits (qubits). Besides 0 or 1, a value can also be 0 and 1 simultaneously so there will be many more possible values that can be obtained. There are three stages in the proposed method, namely the input image stage, the preprocessing stage, and the quantum edge detection stage. Visually, the results show that quantum Canny edge detection can detect more edges compared to classic Canny edge detection, with an average increase of 4.05%.
Quantum image processing and its various applications developed in the last few years. These applications utilize different quantum algorithms for higher accuracy and reduced time complexity compared to classical computing. This paper reviews some applications of quantum image processing like edge extraction, Security, and denoising of images. Applications like feature extraction, machine vision, and feature detection use edge extraction. In this paper, we observe the field of Security by dividing it into two sub-areas, i.e., cryptography and information hiding, used for authentication purposes. For removal of noise signals from input images, the method of denoising is used.
In this work, we propose a novel quantum algorithm for edge detection in digital grayscale images, based on the sequency-ordered Walsh-Hadamard transform. The proposed method significantly improves upon existing quantum techniques for edge detection by using a quantum algorithm for the sequency-ordered Walsh-Hadamard transform, achieving a circuit depth of O(n)\mathcal{O}(n)O(n) (where nnn is the number of qubits). This represents a notable enhancement over the Quantum Fourier Transform (QFT), which has a circuit depth of O(n2)\mathcal{O}(n^{2})O(n2). Furthermore, our approach for edge detection has a computational cost (both gate complexity and quantum circuit depth) of O(log⁡2(N1N2))\mathcal{O}(\log_{2}(N_{1}N_{2}))O(log2​(N1​N2​)) for an image of size N1×N2N_{1}\times N_{2}N1​×N2​, offering a considerable improvement over the Quantum Hadamard Edge Detection (QHED) algorithm, which incurs a cost of O(poly(log⁡2(N1N2)))\mathcal{O}(\text{poly}(\log_{2}(N_{1}N_{2})))O(poly(log2​(N1​N2​))). By integrating a quantum high-pass filter with the sequency-ordered Walsh-Hadamard transform, the algorithm effectively extracts edge information from images. Computational examples are provided to demonstrate the efficacy of the proposed algorithm which provides a better performance in comparison to QHED.