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

Quanmin Guo (), Jiahao Liang and Hanlei Wang
Additional contact information
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

Mathematics, 2023, vol. 11, issue 10, 1-24

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.

Keywords: night vision anti-halation; different source image fusion; low-frequency sequence generation; illuminance estimation; YUV transform; curvelet transform (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/10/2237/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/10/2237/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:10:p:2237-:d:1143850

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().