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    云南民族大學學報(自然科學版)

    2019, v.28;No.118(06) 612-617

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    一種基于總變分與顯著性檢測的紅外與可見光圖像融合方法
    Total variation and saliency detection based infrared and visible image fusion

    馬朝振;劉杰;聶仁燦;
    MA Chao-zhen;LIU Jie;NIE Ren-can;School of Information Science and Engineering, Yunnan University;

    摘要(Abstract):

    紅外與可見光圖像融合是多源信息融合中的一個重要研究內容,它在軍事偵察等方面有著廣泛的應用.本文基于總變分模型和顯著性檢測方法,提出了一種有效的融合方法.首先,通過對紅外與可見光圖像的特征分布考察,構建了一個信息融合的總變分模型.其次,基于亮度對比度的顯著性檢測,給出了總變分模型中保真項權值的估計方法.實驗仿真表明,無論是視覺觀察還是客觀評價,本文的方法均比一些現有方法體現了更好的結果.
    As an important aspect in multi-source information fusion, infrared and visible image fusion has been widely applied in many fields such as military reconnaissance. Based on a total variation model and a saliency detection-based method, this paper proposes an effective fusion method. First, through investigating the feature distribution of infrared and visible images, a total variational model is constructed for our fusion task. Moreover, for the fidelity weights of the total variation, an estimation method is given, based on the saliency detection with luminance contrast(LC). Experimental results show that the proposed method is superior to other methods in both visual observation and objective evaluation.

    關鍵詞(KeyWords): 紅外與可見光圖像融合;總變分模型;顯著性檢測
    infrared and visible image fusion;total variation model;saliency detection

    Abstract:

    Keywords:

    基金項目(Foundation): 國家自然科學基金(61463052,61966037)

    作者(Author): 馬朝振;劉杰;聶仁燦;
    MA Chao-zhen;LIU Jie;NIE Ren-can;School of Information Science and Engineering, Yunnan University;

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    DOI:

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