Zobrazeno 1 - 10
of 1 413
pro vyhledávání: '"Zeng-Yun An"'
Autor:
Chen, Zewen, Wang, Juan, Wang, Wen, Xu, Sunhan, Xiong, Hang, Zeng, Yun, Guo, Jian, Wang, Shuxun, Yuan, Chunfeng, Li, Bing, Hu, Weiming
Existing Image Quality Assessment (IQA) methods achieve remarkable success in analyzing quality for overall image, but few works explore quality analysis for Regions of Interest (ROIs). The quality analysis of ROIs can provide fine-grained guidance f
Externí odkaz:
http://arxiv.org/abs/2411.10161
Autor:
Chen, Zewen, Xu, Sunhan, Zeng, Yun, Guo, Haochen, Guo, Jian, Liu, Shuai, Wang, Juan, Li, Bing, Hu, Weiming, Liu, Dehua, Li, Hesong
With the rising demand for high-resolution (HR) images, No-Reference Image Quality Assessment (NR-IQA) gains more attention, as it can ecaluate image quality in real-time on mobile devices and enhance user experience. However, existing NR-IQA methods
Externí odkaz:
http://arxiv.org/abs/2409.01212
In this paper, we propose a novel adaptive stochastic extended iterative method, which can be viewed as an improved extension of the randomized extended Kaczmarz (REK) method, for finding the unique minimum Euclidean norm least-squares solution of a
Externí odkaz:
http://arxiv.org/abs/2405.19044
The multi-step inertial randomized Kaczmarz (MIRK) method is an iterative method for solving large-scale linear systems. In this paper, we enhance the MIRK method by incorporating the greedy probability criterion, coupled with the introduction of a t
Externí odkaz:
http://arxiv.org/abs/2308.00467
The problem of finding a solution to the linear system $Ax = b$ with certain minimization properties arises in numerous scientific and engineering areas. In the era of big data, the stochastic optimization algorithms become increasingly significant d
Externí odkaz:
http://arxiv.org/abs/2307.16702
In this paper, we analyze the greedy randomized Kaczmarz (GRK) method proposed in Bai and Wu (SIAM J. Sci. Comput., 40(1):A592--A606, 2018) for solving linear systems. We develop more precise greedy probability criteria to effectively select the work
Externí odkaz:
http://arxiv.org/abs/2307.01988
The stochastic heavy ball momentum (SHBM) method has gained considerable popularity as a scalable approach for solving large-scale optimization problems. However, one limitation of this method is its reliance on prior knowledge of certain problem par
Externí odkaz:
http://arxiv.org/abs/2305.05482
We investigate the randomized Kaczmarz method that adaptively updates the stepsize using readily available information for solving inconsistent linear systems. A novel geometric interpretation is provided which shows that the proposed method can be v
Externí odkaz:
http://arxiv.org/abs/2301.00176
Publikováno v:
In Applied Acoustics 15 January 2025 228
Autor:
Ren, Shu-Hui, Zeng, Yun-Chuan, Weinberg, Roberto, Xu, Ji-Feng, Chen, Jian-Lin, Wang, Bao-Di, Huang, Feng, Liu, Xi-Jun, Yu, Hong-Xia, Li, Ming-Jian
Publikováno v:
In Chemical Geology 20 December 2024 670