HDR imaging using remapping and exposure fusion

Ling-feng WU; Na LI; Jun-bao HU

doi:10.37188/CJLCD.2021-0112

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HDR imaging using remapping and exposure fusion

Image Processing | 更新时间：2021-12-10

- HDR imaging using remapping and exposure fusion
- Chinese Journal of Liquid Crystals and Displays Vol. 36, Issue 12, Pages: 1712-1719(2021)
- 作者机构：
  
  1.广东科技学院机电工程学院, 广东东莞 523083
  2.西安邮电大学通信与信息工程学院, 陕西西安 710121
  3.深圳大学微纳光电子学研究院, 广东深圳 518060
- 作者简介：
- 基金信息：
  
  Supported by National Natural Science Foundation of China(41874173);Guangdong University of Science and Technology Youth Project(GKY-2021KYQNK-1)
- DOI：10.37188/CJLCD.2021-0112
  CLC： TP391.41
- Received：21 April 2021，
  
  Revised：19 June 2021，
  
  Published：2021-12
- 稿件说明：
移动端阅览
Ling-feng WU, Na LI, Jun-bao HU. HDR imaging using remapping and exposure fusion[J]. Chinese journal of liquid crystals and displays, 2021, 36(12): 1712-1719.
DOI：

Ling-feng WU, Na LI, Jun-bao HU. HDR imaging using remapping and exposure fusion[J]. Chinese journal of liquid crystals and displays, 2021, 36(12): 1712-1719. DOI： 10.37188/CJLCD.2021-0112.

摘要

传统的高动态范围（HDR）成像方法需采集多幅图像，不仅容易引入视场误差，而且很难适用于新兴的HDR图像采集系统。为此，提出了一种基于重映射和多尺度曝光融合的简单有效的HDR成像方法。该方法只需要采集一组短、长曝光图像，通过人工重映射分别生成一系列多曝光序列；然后根据曝光评价因子求取多曝光序列权重图；接着对权重图进行高斯金字塔分解，并对多曝光序列进行拉普拉斯金字塔分解；最后基于多尺度曝光融合生成一幅细节保持良好的高质量HDR图像。实验结果表明，对比一些主流的算法，所提方法在多个场景测试中性能较好，图像质量在MEF-SSIM评分中得到较大提升。其中，相比基于梯度场的方法提升了13.33%，相比基于深度学习的方法提升了2.62%。所提方法为HDR成像领域提供了新的参考，可潜在地应用于基于CCD/CMOS的成像系统进行高质量HDR图像采集。

Abstract

Traditional high dynamic range (HDR) imaging methods need to capture multiple images

which is not only easy to introduce field of view errors

but also difficult to apply to the emerging image acquisition system. To this end

this paper proposes a simple and effective HDR imaging method based on remapping and exposure fusion. This method only needs to capture a pair of short and long exposure images

and a series of multi-exposure sequence can then be generated through remapping. Next

according to the exposure evaluation

the weight map of the sequence can be obtained. The weight map is decomposed by Gaussian pyramid

and the multi-exposure sequence is decomposed by Laplacian pyramid. Finally

a high-quality HDR image with good details is generated based on multi-scale fusion. Experimental results show that the proposed method performs better in multiple scene tests

and the image quality is greatly improved in the MEF-SSIM score compared with some mainstream algorithms. The image qulity is improved by 13.33% compared with GF-MEF

and improved by 2.62% compared with CNN-MEF. The proposed method provides a new reference for the field of HDR imaging

and can be potentially applied to CCD/CMOS-based imaging systems for high-quality image acquisition.

关键词

Keywords

references

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