The proposed study investigates the use of the Novel Enhanced Quantum Representation (NEQR) in Quantum picture Processing (QIP) for image processing. It focusses on the way the FREI-CHEN edge detection operator, which is an enhanced Prewitt operator, behaves on quantum images. The Frei-Chen operator improves edge identification more precisely than the Prewitt operator, Sobel operator and Kirsch operator. The work shows significant improvements by applying the Frei-Chen operator on quantum-converted pictures, obtaining a Mean Square Error (MSE) of about 19.4932 and a Peak Signal-to-Noise Ratio (PSNR) of around 35.4453 dB.
The proposed study investigates the use of the Novel Enhanced Quantum Representation (NEQR) in Quantum picture Processing (QIP) for image processing. It focusses on the way the FREI-CHEN edge detection operator, which is an enhanced Prewitt operator, behaves on quantum images. The Frei-Chen operator improves edge identification more precisely than the Prewitt operator, Sobel operator and Kirsch operator. The work shows significant improvements by applying the Frei-Chen operator on quantum-converted pictures, obtaining a Mean Square Error (MSE) of about 19.4932 and a Peak Signal-to-Noise Ratio (PSNR) of around 35.4453 dB.
In this work, a quantum image processing algorithm is developed using the edge extraction method together with the Kirsch operator. In our approach, novel enhanced quantum representation (NEQR) is employed as the image representation model for processing quantum image, which generates results of edge extraction using the Kirsch operator. The proposed algorithm can perform real-time image processing with high accuracy. We carry out the design, analyses, and simulations of quantum circuits based on our approach, which shows that the image processing speed and acuracy are much better than the classical edge extraction algorithms.
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.
This study presents a comparative evaluation of three quantum image representation techniques: FRQI, QPIE, and NEQR. The Baboon image is used as a benchmark, and performance is assessed through PSNR, MSE, SSIM, and execution time. Simulations were conducted using a noiseless quantum simulator to ensure fair comparison across methods. Results indicate that QPIE achieves the best reconstruction quality, with PSNR = 117.97, MSE = 1.037, and SSIM = 0.975 for the 8 x 8 image, while maintaining strong performance for larger sizes. In contrast, FRQI shows lower quality and high execution time. NEQR is computationally faster (4.85 s) but delivers moderate quality (PSNR = 79.38, SSIM = 0.859). Additionally, QPIE requires fewer qubits, making it more resource-efficient. Overall, QPIE emerges as the most effective representation, striking a balance between image quality, computational cost, and qubit utilization. These findings highlight QPIE as a promising candidate for future quantum image processing tasks such as compression, edge detection, and negative transformation.
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.
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%.