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Updated: Sep 14, 2025

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像素自适应深度展开的神经网络与状态空间模型用于图像脱轨

Yao Xiao1, Youshen Xia2

  • 1College of Artificial Intelligence, Anhui University, HeFei, China; College of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China.

Neural networks : the official journal of the International Neural Network Society
|July 18, 2025
PubMed
概括

这项研究介绍了一种新的像素自适应深度展开网络,用于有效的图像脱轨. 该方法通过改善整体结构感知和自适应步骤大小控制来提高视觉质量,优于现有技术.

关键词:
深度展开的神经网络.图像恢复 图像恢复雨线条的去除 雨线条的去除国家空间模型

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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