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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 8

Unsupervised raw infrared image enhancement based on layer decomposition

Li Qintong
Ma Yong
Huang Jun
Wang Ge
Zhang Can
Electronic Information School, Wuhan University, Wuhan 430072, China
DOI: 10.19734/j.issn.1001-3695.2024.11.0509

Abstract

The raw infrared images captured by infrared imaging devices commonly suffer from issues such as low contrast and poor target saliency. Traditional enhancement techniques struggle to adaptively handle uneven illumination in complex scenes, while deep learning-based methods excessively rely on high-quality labeled data. To address these issues, an unsupervised learning and layer decomposition-based infrared image enhancement method is proposed. This method employs a layer decomposition strategy to separate the input image into a base layer and a detail layer. By introducing a multi-scale enhancement network, it adaptively adjusts the brightness and contrast of the base layer, optimizing both global and local information, and then fuses it with the detail layer to generate the enhanced image. Additionally, multiple no-reference loss functions are utilized to jointly optimize contrast, brightness, and image entropy, significantly improving visual quality while avoiding dependency on paired data. Experimental results on a dataset constructed using the FLIR A700 infrared camera demonstrate that the proposed method outperforms several existing algorithms in terms of contrast enhancement and detail preservation, effectively enhancing target saliency and validating the method's effectiveness and advancement.

Foundation Support

国家自然科学基金资助项目(62473297,62475199,62075169,U23B2050)
珠海市产学研合作项目(2220004002828)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.11.0509
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 8

Publish History

[2025-03-21] Accepted Paper

Cite This Article

李钦童, 马泳, 黄珺, 等. 基于层次分解的无监督原始红外图像增强 [J]. 计算机应用研究, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0509. (Li Qintong, Ma Yong, Huang Jun, et al. Unsupervised raw infrared image enhancement based on layer decomposition [J]. Application Research of Computers, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0509. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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