Image Enhancement Model Based on Multi-scale Dynamic Filtering

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Image Enhancement Model Based on Multi-scale Dynamic Filtering

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[1]YIN Yi,LYU Pei,LI Kaijiang,et al.Image Enhancement Model Based on Multi-scale Dynamic Filtering[J].Journal of Zhengzhou University (Engineering Science),2026,47(XX):1-8.[doi:10.13705/j.issn.1671-6833.2025.03.016]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 47 Number of periods: 2026 XX Page number: 1-8 Column: Public date: 2026-09-10

Title:: Image Enhancement Model Based on Multi-scale Dynamic Filtering

Author(s):: YIN Yi; LYU Pei; LI Kaijiang; ZHENG Haokun; XU Hao; CHEN Mengjie; School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China

Keywords:: image enhancement; high-pass filtering; low-pass filtering; multi-scale fusion; frequency domain transformation

CLC:: TP37TP391. 9

DOI:: 10.13705/j.issn.1671-6833.2025.03.016

Abstract:: To improve the effectiveness of the active collision avoidance strategy, a risk assessment method for collision time margin is proposed. The three-degree-of-freedom vehicle model and the Dugoff tire model were established, and the state parameters were calculated to obtain the normalized tire force. The pavement adhesion coefficient estimator is designed based on the traceless Kalman filtering algorithm, and the effectiveness of the adhesion coefficient estimator is verified through simulation. The road adhesion coefficient was added to the safety distance model to address the limitations of the traditional collision avoidance model, which only considers the position and the vehicle movement conditions. Generate the active collision avoidance path using a fivetic polynomial and calculate the required safe steering distance. Based on the risk assessment method, a collision avoidance mode selection strategy was designed, enabling intelligent vehicles to select the appropriate collision avoidance mode according to the kinematic relationship with obstacles. The longitudinal control based on the vehicle inverse dynamic model and the lateral control using MPC are adopted to decouple the control of intelligent vehicles. The effectiveness of the collision avoidance strategy was verified through the joint simulation experiment of Carsim and Simulink and the real vehicle test.

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Last Update: 2026-01-14