Quantum image processing, which is the application of quantum theory in image processing, has attracted increasing attention in recent years. In this paper, we propose a framework of Laplacian of Gaussian (LoG) edge detection algorithm for quantum images. The LoG is commonly employed as a second-order edge detector in image processing. Firstly, discrete LoG mask is obtained from the continuous Gaussian operator within certain detection error. Secondly, filter the quantum image with the LoG mask. Thirdly, to obtain the edge image, the searching for zero-crossing point of the filtered image is proposed. Finally, the time complexity is analyzed, which shows that this quantum LoG operator has polynomial-time complexity.
Quantum image processing, which is the application of quantum theory in image processing, has attracted increasing attention in recent years. In this paper, we propose a framework of Laplacian of Gaussian (LoG) edge detection algorithm for quantum images. The LoG is commonly employed as a second-order edge detector in image processing. Firstly, discrete LoG mask is obtained from the continuous Gaussian operator within certain detection error. Secondly, filter the quantum image with the LoG mask. Thirdly, to obtain the edge image, the searching for zero-crossing point of the filtered image is proposed. Finally, the time complexity is analyzed, which shows that this quantum LoG operator has polynomial-time complexity.
Abstract With the increase in the quantity and quality of images that need to be processed, there are higher requirements for the speed of image processing. Thus, it is necessary to speed-up the image processing algorithms. In this paper, we design a quantum image edge detection algorithm based on Laplacian of Gaussian (LoG) operator, which has significant acceleration due to the quantum parallelism. Firstly, the quantum circuit of the novel enhanced quantum image representation (NEQR) model is improved to represent multiple quantum images simultaneously with lower time complexity. Secondly, the quantum subtractor and quantum comparator are optimized. Thirdly, quantum adder, quantum multiplier, quantum comparator, and quantum subtractor are adopted to design the quantum circuit of LoG operator and zero-crossing detector, which constitute the quantum edge detection algorithm. Finally, the algorithm is simulated on the IBM Quantum Experience (IBM Q) platform to verify its feasibility. Compared with the existing state-of-the-art algorithms, the proposed quantum image edge detection algorithm has significant improvement in time complexity.
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.
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.
Line detection is a fundamental technique in image processing. It can extract the required information, while the information that does not need attention can be ignored, thus reducing the amount of data. At the same time, line detection is also the basis of image segmentation and plays an important role in this process. In this paper, we implement a quantum algorithm based on a line detection mask for novel enhanced quantum representation (NEQR). We build a quantum algorithm for line detection in different directions and design a quantum circuit for line detection. The detailed module designed is also provided. On a classical computer, we simulate the quantum method, and the simulation results prove the feasibility of the quantum method. By analyzing the complexity of quantum line detection, we find that the computation complexity of the proposed method is improved compared to some similar edge detection algorithms.
In recent years, quantum mechanics has been connected with computer science, information theory in communication and digital signal processing. Developing new computing methods and signal processing algorithms by borrowing the principle of quantum mechanics is a very interesting and new research topic. In this research, a quantum digital image processing algorithms will be presented and compared to five other edge detection algorithms, such as Roberts, Sobel, Prewitt, Laplacian of Gaussian, and Canny. Therefore an edge detection method comparison using MATLAB is done in order to find the best edge detection method for qualitative evaluation, namely sensitivity and specificity. From all the comparisons process, the best edge detection method for sensitivity and specificity is known. For the sensitivity, Canny’s edge detection algorithm without quantum enhancement is the best method with sensitivity value amount 83.33% and for the specificity, Roberts edge detection algorithm using quantum enhancement is the best method with specificity value amount 83.33%.
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.