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Night Vision Anti-Halation Algorithm of Different-Source Image Fusion Based on Low-Frequency Sequence Generation

Author

Listed:
  • Quanmin Guo

    (School of Electronic and Information Engineering, Xi’an Technological University, Xi’an 710021, China)

  • Jiahao Liang

    (School of Electronic and Information Engineering, Xi’an Technological University, Xi’an 710021, China)

  • Hanlei Wang

    (School of Electronic and Information Engineering, Xi’an Technological University, Xi’an 710021, China)

Abstract

The abuse of high beam lights dazzles the opposite drivers when the vehicles meet at night, which can easily cause traffic accidents. The existing night vision anti-halation algorithms based on different-source image fusion can eliminate halation and obtain fusion images with rich color and details. However, the algorithms mistakenly eliminate some high-brightness important information. In order to address the problem, a night vision anti-halation algorithm based on low-frequency sequence generation is proposed. The low-frequency sequence generation model is constructed to generate image sequences with different degrees of halation elimination. According to the estimated illuminance for image sequences, the proposed sequence synthesis based on visual information maximization assigns a large weight to the areas with good brightness so as to obtain the fusion image without halation and with rich details. In four typical halation scenes covering most cases of night driving, the proposed algorithm effectively eliminates halation while retaining useful high-brightness information and has better universality than the other seven advanced comparison algorithms. The experimental results show that the fusion image obtained by the proposed algorithm is more suitable for human visual perception and helps to improve night driving safety.

Suggested Citation

  • Quanmin Guo & Jiahao Liang & Hanlei Wang, 2023. "Night Vision Anti-Halation Algorithm of Different-Source Image Fusion Based on Low-Frequency Sequence Generation," Mathematics, MDPI, vol. 11(10), pages 1-24, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2237-:d:1143850
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    References listed on IDEAS

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    1. Min Li & Dachuan Xu & Dongmei Zhang & Juan Zou, 2020. "The seeding algorithms for spherical k-means clustering," Journal of Global Optimization, Springer, vol. 76(4), pages 695-708, April.
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