计算机应用 ›› 2019, Vol. 39 ›› Issue (6): 1804-1809.DOI: 10.11772/j.issn.1001-9081.2018112284

• 虚拟现实与多媒体计算 • 上一篇    下一篇

基于模拟多曝光融合的低照度图像增强方法

司马紫菱1,2, 胡峰1,2   

  1. 1. 重庆邮电大学 计算机科学与技术学院, 重庆, 400065;
    2. 计算智能重庆市重点实验室(重庆邮电大学), 重庆, 400065
  • 收稿日期:2018-11-19 修回日期:2019-01-18 发布日期:2019-06-17 出版日期:2019-06-10
  • 通讯作者: 司马紫菱
  • 作者简介:司马紫菱(1996-),女,湖北天门人,硕士研究生,主要研究方向:智能信息处理;胡峰(1978-),男,湖北天门人,教授,博士,主要研究方向:数据挖掘、Rough集、粒计算、智能信息处理。
  • 基金资助:
    国家重点研发计划项目(2018YFC0808305);国家自然科学基金资助项目(61751312,61533020,61309014);重庆市重点产业共性关键技术创新专项(cstc2017zdcy-zdyfX0001,cstc2017zdcy-zdzx0046);重庆市基础科学与前沿技术研究专项(cstc2017jcyjAX0408)。

Low-light image enhancement method based on simulating multi-exposure fusion

SIMA Ziling1,2, HU Feng1,2   

  1. 1. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
    2. Chongqing Key Laboratory of Computational Intelligence(Chongqing University of Posts and Telecommunications), Chongqing 400065, China
  • Received:2018-11-19 Revised:2019-01-18 Online:2019-06-17 Published:2019-06-10
  • Supported by:
    This work is partially supported by the National Key Research and Development Program of China (2018YFC0808305), the National Natural Science Foundation of China (61751312, 61533020, 61309014), the Chongqing Special Fund for Common Key Technology Innovations in Key Industries (cstc2017zdcy-zdyfX0001, cstc2017zdcy-zdzx0046), the Chongqing Research Program of Basic Research and Frontier Technology (cstc2017jcyjAX0408).

摘要: 针对部分低照度图像整体亮度偏暗、对比度差和视觉信息偏弱等问题,提出一种基于模拟多曝光融合的低照度图像增强方法。首先,利用改进的变分Retinex模型和形态学的结合产生基准图来保证曝光图像集中的主体信息;其次,结合Sigmoid函数和伽马矫正构造新的光照补偿归一化函数,同时提出了一种基于高斯引导滤波的反锐化掩模算法,用于调整基准图的细节;最后,分别从亮度、色调和曝光率设计曝光图集的加权值,通过多尺度融合得到最终增强结果,有效地避免了增强结果中的光晕和颜色失真。在不同的公开数据集上的实验结果表明,与传统的低照度图像增强方法进行相比,所提方法降低了亮度失真率,提升了视觉信息保真度。该方法能够有效地保留视觉信息,有利于实现低照度图像增强的实时性应用。

关键词: 低照度图像, Retinex理论, 曝光融合, 细节调整, 图像增强

Abstract: Aiming at the problems of low luminance, low contrast and poor visual information, a low-light image enhancement method based on simulating multi-exposure fusion was proposed. Firstly, the improved variational Retinex model and morphology were combined to generate the reference map to ensure the subject information in the exposed image set. Then, a new illumination compensation normalization function was constructed by combining Sigmoid function and gamma correction. At the same time, an unsharp masking algorithm based on Gaussian guided filtering was proposed to adjust the details of the reference map. Finally, the weighted values of exposed image set were designed from luminance, chromatic information and exposure rate respectively, and the final enhancement result was obtained through multi-scale fusion with effective avoidance of halo phenomenon and color distortion. The experimental results on different public datasets show that, compared with the traditional low-light image enhancement method, the proposed method has reduced the lightness distortion rate and increased the visual information fidelity. The proposed method can effectively preserve visual information, which is conducive to the real-time application of low-light image enhancement.

Key words: low-light image, Retinex theory, exposure fusion, detail adjustment, image enhancement

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