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基于一致性敏感哈希块匹配的HDR图像去伪影融合方法

朱雄泳 吴炆芳 陆许明 谭洪舟 邹兵兵

朱雄泳, 吴炆芳, 陆许明, 谭洪舟, 邹兵兵. 基于一致性敏感哈希块匹配的HDR图像去伪影融合方法. 自动化学报, 2020, 46(7): 1496-1506. doi: 10.16383/j.aas.2018.c180003
引用本文: 朱雄泳, 吴炆芳, 陆许明, 谭洪舟, 邹兵兵. 基于一致性敏感哈希块匹配的HDR图像去伪影融合方法. 自动化学报, 2020, 46(7): 1496-1506. doi: 10.16383/j.aas.2018.c180003
ZHU Xiong-Yong, WU Wen-Fang, LU Xu-Ming, TAN Hong-Zhou, ZOU Bing-Bing. High-Dynamic-Range Image De-ghosting Fusion Method Based on Coherency Sensitive Hashing Patch-Match. ACTA AUTOMATICA SINICA, 2020, 46(7): 1496-1506. doi: 10.16383/j.aas.2018.c180003
Citation: ZHU Xiong-Yong, WU Wen-Fang, LU Xu-Ming, TAN Hong-Zhou, ZOU Bing-Bing. High-Dynamic-Range Image De-ghosting Fusion Method Based on Coherency Sensitive Hashing Patch-Match. ACTA AUTOMATICA SINICA, 2020, 46(7): 1496-1506. doi: 10.16383/j.aas.2018.c180003

基于一致性敏感哈希块匹配的HDR图像去伪影融合方法

doi: 10.16383/j.aas.2018.c180003
基金项目: 

国家自然科学基金 61473322

国家自然科学基金 61772140

广东省省级科技计划项目 2017A010101021

广东省自然科学基金项目 2018A0303130169

广东省自然科学基金博士启动项目 2016A030310335

详细信息
    作者简介:

    朱雄泳  博士, 广东第二师范学院计算机科学系教师.主要研究方向为数字图像处理, 视频信号处理, 计算机视觉.E-mail: zhuxiongyong@gdei.edu.cn

    吴炆芳  中山大学电子与信息工程学院, 硕士研究生.主要研究方向为数字图像处理. E-mail: Wuwf828@163.com

    陆许明  博士, 广东第二师范学院计算机科学系讲师.主要研究方向为数字图像处理, 视频信号处理, 无线通信, 集成电路设计.E-mail: luxuming@gdei.edu.cn

    邹兵兵  中山大学电子与信息工程学院硕士研究生.主要研究方向为数字图像处理.E-mail: zoubingbing1991@163.com

    通讯作者:

    谭洪舟  中山大学电子与信息工程学院教授.主要研究方向为宽带通信, 信号处理, 复杂系统辨识及建模, 半导体集成电路设计.本文通信作者.E-mail: issthz@mail.sysu.edu.cn

High-Dynamic-Range Image De-ghosting Fusion Method Based on Coherency Sensitive Hashing Patch-Match

Funds: 

National Natural Science Foundation of China 61473322

National Natural Science Foundation of China 61772140

Science and Technology Planning Project of Guangdong Province 2017A010101021

Natural Science Foundation of Guangdong Province 2018A0303130169

The Ph. D. Start-up Fund of Natural Science Foundation of Guangdong Province 2016A030310335

More Information
    Author Bio:

    ZHU Xiong-Yong Ph. D., lecturer in the Department of Computer Science, Guangdong University of Education. His research interest covers digital image processing, video signal processing, and computer vision.

    WU Wen-Fang Master student at the School of Electronics and Information Technology, Sun Yat-Sen University. Her main research interest is digital image processing.

    LU Xu-Ming Ph. D., lecturer in the Department of Computer Science, Guangdong University of Education.His research interest covers digital image processing, video signal processing, wireless communications, and IC design.

    ZOU Bing-Bing Master student at the School of Electronics and Information Technology, Sun Yat-Sen University. His main research interest is digital image processing.

    Corresponding author: TAN Hong-Zhou Professor at the School of Electronics and Information Technology, Sun Yat-Sen University. His research interest covers broadband communications, signal processing, identiflcation and modeling of complex systems, and semicondector IC design. Corresponding author of this paper.
  • 摘要: 高动态范围(High dynamic range, HDR)图像成像技术的出现, 为解决由于采集设备动态范围不足而导致现有数字图像动态范围有限的问题提供了一条切实可行的思路.合成高动态范围图像的过程中因相机抖动或运动物体所造成的模糊和伪影问题, 可通过块匹配对多曝光图像序列进行去伪影融合加以解决.但对于具有复杂运动变化的真实场景, 现有的去伪影融合方法准确度和效率仍存在不足.为此, 本文结合相机响应函数和一致性敏感哈希提出了一种高动态图像去伪影融合方法.仿真结果表明, 该方法有效降低了计算复杂度, 具有较好的鲁棒性, 在有效去除伪影的同时提升了高动态范围图像质量.
    Recommended by Associate Editor LIU Qing-Shan
    1)  本文责任编委 刘青山
  • 图  1  不同加权函数的曲线

    Fig.  1  Curves of different weighted functions

    图  2  WH变换投影向量

    说明:其中s = [1], 白色表示其像素值为1, 黑色表示为-1, 且WH向量以递增的空间变化频率排序

    Fig.  2  WH transform projection vector

    图  3  图像块的候选类型

    Fig.  3  Candidate type of image block

    图  4  基于相机响应函数和CSH的去伪影融合方法流程图

    Fig.  4  Flow chart of de-ghosting fusion method based on CRF and CSH

    图  5  Forrest多曝光图像序列(秒)

    Fig.  5  Forrest multi-exposure image sequence (s)

    图  6  Arch多曝光图像序列

    Fig.  6  Arch multi-exposure image sequence

    图  7  fast_abrupt_motion图像序列

    Fig.  7  fast_abrupt_motion multi-exposure image sequence

    图  8  对Forrest图像序列使用不同方法得到的相机响应函数

    Fig.  8  The obtained CRF curve by using different methods in Forrest Sequence

    图  9  Forrest图像序列去伪影融合方法的对比

    Fig.  9  Comparison of different de-ghosting algorithms in Forrest sequence

    图  10  Forrest图像序列去伪影融合方法的局部细节对比

    Fig.  10  Local detail comparison of de-ghosting fusion methods in Forrest sequence

    图  11  SculptureGarden图像序列去伪影融合方法的对比

    Fig.  11  Comparison of different de-ghosting algorithms in SculptureGarden

    图  12  SculptureGarden图像序列去伪影融合方法的局部细节对比

    Fig.  12  Local detail comparison of de-ghosting fusion methods in SculptureGarden sequence

    图  13  children_and_slide图像序列去伪影融合方法的对比

    Fig.  13  Comparison of different de-ghosting algorithms in children_and_slide

    图  14  children_and_slide图像序列去伪影融合方法的局部细节对比

    Fig.  14  Local detail comparison of de-ghosting fusion methods in children_and_slide

    图  15  fast_abrupt_motion图像序列去伪影融合方法的对比

    Fig.  15  Comparison of different de-ghosting algorithms in fast_abrupt_motion

    图  16  fast_abrupt_motion图像序列去伪影融合方法的对比

    Fig.  16  Comparison of different de-ghosting algorithms in fast_abrupt_motion

    表  1  根据观察1~4组合得到3种图像A中块a的候选块

    Table  1  According to the observation 1 ~ 4, 3 kinds of candidates of block a in A is obtained

    类型 定义 使用的观察类型
    1 gB-1(gA(a)) 1和3
    2 gB-1(gB(Right(Cand(Left(a))))) 3和4
    3 Cand(gA-1(gA(a))) 2
    下载: 导出CSV

    表  2  改进前后算法性能对比

    Table  2  Performance comparison of Debevec$ ' $s and our algorithm

    多组图像序列 图像分辨率 Debevec方法(s) 改进方法(s) 平均误差
    Forrest $ 683 \times 1\, 024 \times 3 $ 0.555 0.368 $ 12.36\% $
    Arch $ 1\, 024 \times 669 \times 3 $ 1.249 0.633 $ 7.68\% $
    fast_abrupt_motion $ 1\, 080 \times 1\, 920 \times 3 $ 0.445 0.275 $ 11.44\% $
    下载: 导出CSV

    表  3  对于不同图像序列, 不同的去伪影方法评价指标

    Table  3  For difierent image sequences, performe of difierent de-ghosting methods

    图像序列 方法 平均亮度 对比度 信息熵 运行时间(s)
    Forrest RM 106.101 25.583 7.421 37.103
    HU 132.799 27.603 7.546 122.547
    PSSV 97.636 48.641 7.838 16.307
    Ours 132.265 33.263 7.635 7.848
    SculptureGarden RM 107.484 18.151 7.551 60.981
    HU 122.785 21.187 7.325 182.470
    PSSV 87.305 20.096 7.332 22.359
    Ours 127.772 23.920 7.553 8.752
    children_and_slide RM 90.813 11.943 7.491 86.038
    HU 91.018 14.363 7.649 133.990
    PSSV 85.190 15.499 7.717 21.235
    Ours 93.342 14.949 7.668 8.194
    fast_abrupt_motion RM 109.083 10.365 7.335 97.803
    HU 122.893 13.337 7.662 152.608
    PSSV 102.323 14.699 7.781 20.185
    Ours 122.920 12.415 7.636 7.630
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-01-04
  • 录用日期:  2018-04-22
  • 刊出日期:  2020-07-24

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