文献类型：期刊文献

英文题名：Discrete zeroing neural dynamic with noise tolerance for image deblurring

作者：Lin, Cong[1];Zhuang, Fenghao[1];Li, Jiahao[1];Jiang, Chengze[2];Wu, Yuanyuan[1]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[2]Southeast Univ, Sch Cyber Sci & Engn, Nanjing 211189, Peoples R China

年份：2026

卷号：296

外文期刊名：EXPERT SYSTEMS WITH APPLICATIONS

收录：SCI-EXPANDED(收录号：WOS:001537189500001)、、WOS

基金：This work was supported in part by the National Natural Science Foundation of China (62272109) , the Stable Supporting Fund of Acoustic Science and Technology Laboratory (JCKYS2024604SSJS00301) , the Open Fund of Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching (Grant NO. 2023B1212030003) , the program for scientific research startup funds of Guangdong Ocean University (060302112405) , and the Undergraduate Innovation Team Project of Guangdong Ocean University (CXTD2024011) .

语种：英文

外文关键词：Image deblurring; Noise-tolerance; Neural network; Neural dynamic; Learning-free

外文摘要：As the demand for high-quality images continues to grow, image deblurring has become a fundamental challenge in computer vision. Although numerous effective deblurring methods have been proposed, one critical area remains largely unexplored: the interference caused by environmental noise. It is well known that noise can perturb solution systems, leading to instability or even collapse. Current mainstream methods, such as deep learning-based approaches, struggle to address such perturbations effectively. Additionally, these methods require large datasets for training and optimization, which incur significant computational cost and time. Without sufficient data, their robustness and deblurring performance are greatly limited. To address these challenges, we consider an alternative approach: a learning-free neural network, called neural dynamic. Our method employs a dynamic solving mechanism capable of addressing potential static optimization problems, while its integral term enhances noise resistance. To further adapt this framework for practical engineering applications, we developed a Taylor expansion-based discretization scheme called Taylor-type 6-instant Noise-Tolerance Zeroing neural Dynamic (T6NTZD). This model not only improves noise resistance but also achieves lightweight design and real-time processing. By introducing this approach, we aim to fill a significant gap in the field of image deblurring. Finally, through a detailed theoretical analysis from a continuous perspective and a comprehensive comparison with 12 neural dynamics models, the superiority of this method is clearly demonstrated. The key advantages of our model are summarized as follows: strong robustness, lightweight design, and the elimination of the need for data-intensive learning.