Computer vision detection technology is one of the most popular topics in the field of computer vision. With the continuous improvement of the relevant algorithm and the performance-price ratio of the corresponding imaging equipment, the corresponding computer vision detection algorithm is also constantly upgraded and deepened. Computer vision detection technology is mainly used in transportation, public security, national defense and military fields, but the pixel accuracy of traditional computer vision detection technology has been unable to meet today's accuracy requirements. In this paper, firstly, the quantum denoising algorithm based on dual-tree and dual-density wavelet transform is used to realize the combination of quantum image coding expression and wavelet transform, and finally achieve a more detailed and accurate description of the image and realize the noise reduction of the image. In order to further realize sub-pixel image processing, cubic spline interpolation edge detection algorithm will be added to wavelet transform, which mainly calculates the zeros of the second-order function corresponding to the cubic spline function on both sides of the image edge points, so as to realize sub-pixel location of the image edge points. Finally, by comparing with the traditional pixel accuracy detection algorithms, it can be found that the proposed subpixel computer vision detection algorithm based on wavelet transform has good robustness, and its computing time is relatively faster, so it will have better adaptability in practical applications. INDEX TERMS Wavelet transform, vision detection algorithm, quantum denoising algorithm, subpixel vision detection algorithm.
For solving the large sparse linear systems with
2
×
2
block structure, the generalized successive overrelaxation (GSOR) iteration method is an efficient iteration method. Based on the GSOR method, the PGSOR method introduces a preconditioned matrix with a new parameter for the coefficient matrix which can enhance the efficiency. To solve the nonlinear systems in which the Jacobian matrices are complex and symmetric with the block two-by-two form, we try to use the PGSOR method as an inner iteration, with the help of the modified Newton method as an efficient outer iteration method. This new method is called the modified Newton-PGSOR (MN-PGSOR) method. Local convergence properties of the MN-PGSOR are analyzed under the Hölder condition. Finally, we give the comparison of our new method with some previous methods in the numerical results. The MN-PGSOR method is superior in both iteration steps and computing time.
This paper proposes the modified generalization of the HSS (MGHSS) to solve a large and sparse continuous Sylvester equation, improving the efficiency and robustness. The analysis shows that the MGHSS converges to the unique solution of AX + XB = C unconditionally. We also propose an inexact variant of the MGHSS (IMGHSS) and prove its convergence under certain conditions. Numerical experiments verify the efficiency of the proposed methods.
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