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Infrared image enhancement algorithm based on detail enhancement guided image filtering

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Abstract

Because of the unique imaging mechanism of infrared (IR) sensors, IR images commonly suffer from blurred edge details, low contrast, and poor signal-to-noise ratio. A new method is proposed in this paper to enhance IR image details so that the enhanced images can effectively inhibit image noise and improve image contrast while enhancing image details. First, for the traditional guided image filter (GIF) applied to IR image enhancement is prone to halo artifacts, this paper proposes a detail enhancement guided filter (DGIF). It mainly adds the constructed edge perception and detail regulation factors to the cost function of the GIF. Then, according to the visual characteristics of human eyes, this paper applies the detail regulation factor to the detail layer enhancement, which solves the problem of amplifying image noise using fixed gain coefficient enhancement. Finally, the enhanced detail layer is directly fused with the base layer so that the enhanced image has rich detail information. We first compare the DGIF with four guided image filters and then compare the algorithm of this paper with three traditional IR image enhancement algorithms and two IR image enhancement algorithms based on the GIF on 20 IR images. The experimental results show that the DGIF has better edge-preserving and smoothing characteristics than the four guided image filters. The mean values of quantitative evaluation of information entropy, average gradient, edge intensity, figure definition, and root-mean-square contrast of the enhanced images, respectively, achieved about 0.23%, 3.4%, 4.3%, 2.1%, and 0.17% improvement over the optimal parameter. It shows that the algorithm in this paper can effectively suppress the image noise in the detail layer while enhancing the detail information, improving the image contrast, and having a better visual effect.

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Data availability statement

One dataset is available in [DATASETS CIDI] repository, [http://www.cidis.espol.edu.ec/es/content/dataset-far-infrared-images]. Another dataset is available in the [TNO Image fusion dataset] repository, [https://figshare.com/articles/dataset/TNO_Image_Fusion_Dataset/1008029]. An additional portion of the dataset analyzed during the current study is available from the corresponding authors upon reasonable request.

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Funding

National Nature Science Foundation of China, 61971373, Meijing Gao, Natural Science Foundation of Hebei Province-China, C2020203010, Ailing Tan, Hebei Innovative Training Program for Doctoral Candidate of China, CXZZBS2022148, Shiyu Li.

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Authors and Affiliations

  1. Laboratory for Special Fiber and Fiber Sensor of Hebei Province, School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China

    Ailing Tan, Hongping Liao, Bozhi Zhang, Shiyu Li, Yang Bai & Zehao Liu

  2. School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, 100081, China

    Meijing Gao

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  1. Ailing Tan
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  2. Hongping Liao
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  3. Bozhi Zhang
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  4. Meijing Gao
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  5. Shiyu Li
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  6. Yang Bai
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  7. Zehao Liu
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Correspondence to Meijing Gao.

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The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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This work was supported by National Nature Science Foundation of China (61971373) and Natural Science Foundation of Hebei Province-China (C2020203010), and the Hebei Innovative Training Program for Doctoral Candidate of China (CXZZBS2022148).

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Tan, A., Liao, H., Zhang, B. et al. Infrared image enhancement algorithm based on detail enhancement guided image filtering. Vis Comput 39, 6491–6502 (2023). https://doi.org/10.1007/s00371-022-02741-6

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