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基于图像块分解融合的水下标定图像增强

常志文 王立忠 梁晋 李壮壮 龚春园 巫志辉 徐建宁

常志文, 王立忠, 梁晋, 李壮壮, 龚春园, 巫志辉, 徐建宁. 基于图像块分解融合的水下标定图像增强[J]. 中国光学(中英文). doi: 10.37188/CO.2023-0218
引用本文: 常志文, 王立忠, 梁晋, 李壮壮, 龚春园, 巫志辉, 徐建宁. 基于图像块分解融合的水下标定图像增强[J]. 中国光学(中英文). doi: 10.37188/CO.2023-0218
CHANG Zhi-wen, WANG Li-zhong, LIANG Jin, LI Zhuang-zhuang, GONG Chun-yuan, WU Zhi-hui, XU Jian-ning. Underwater calibration image enhancement based on image block decomposition and fusion[J]. Chinese Optics. doi: 10.37188/CO.2023-0218
Citation: CHANG Zhi-wen, WANG Li-zhong, LIANG Jin, LI Zhuang-zhuang, GONG Chun-yuan, WU Zhi-hui, XU Jian-ning. Underwater calibration image enhancement based on image block decomposition and fusion[J]. Chinese Optics. doi: 10.37188/CO.2023-0218

基于图像块分解融合的水下标定图像增强

doi: 10.37188/CO.2023-0218
基金项目: 国家重点研发计划项目(No. 2022YFB4601802);国家自然科学基金资助项目(No. 52275543)
详细信息
    作者简介:

    王立忠(1968—),男,山东梁山人,博士,教授,博士生导师,2004年于西安交通大学获得博士学位,主要从事三维光学测量技术的研究。E-mail:wanglz@mail.xjtu.edu.cn

  • 中图分类号: TP394.1;TH691.9

Underwater calibration image enhancement based on image block decomposition and fusion

Funds: Supported by the National Key R&D Program of China (No. 2022YFB4601802); National Natural Science Foundation of China (No. 52275543)
More Information
  • 摘要:

    针对水下视觉测量中相机标定采集的水下标定图像退化造成标志点信息缺损的问题,提出了一种基于图像块分解融合的水下标定图像增强算法。首先,针对水下标定图像光照不均匀造成图像去雾困难的问题,基于同态滤波实现图像分割并计算全局背景光强,以实现图像去雾。然后,针对水下图像去雾后仍然存在噪声、模糊、光照不均匀等问题,分别进行对比度增强与细节信息增强以获得两幅互补增强图像,将互补图像划分成多个图像块,将图像块分解为平均强度、信号强度和信号结构3个独立分量,3个分量分开融合并求解最终增强图像。最后,采用主客观评价及标志点检测实验评价水下标定图像增强后的质量。实验结果表明,本文方法的视觉效果及客观评价得分均高于UDCP、MSR及ACDC方法,浑浊度为7.6NTU、11.4NTU、15.7NTU、18.4NTU时,标志点检测数量分别提高了2.0%、2.3%、9.3%、21.2%。因此,本文方法可以有效提高水下标定图像质量,为水下视觉测量提供一种稳定可靠的水下标定图像增强方法。

     

  • 图 1  水下成像模型

    Figure 1.  Underwater imaging model

    图 2  (a) 水下标定图像及(b) 最小值滤波结果

    Figure 2.  (a) Underwater calibration image and (b) minimum value filtering results

    图 3  水下光照不均匀原理示意图。(a)光源垂直照射示意图;(b)水下光照不均匀图像

    Figure 3.  Schematic diagram of uneven underwater illumination principle. (a) Schematic diagram of light source vertical irradiation; (b) underwater uneven illumination image

    图 4  水下标定图像及图像分割结果。(a) 水下标定图像;(b) Ostu法分割结果;(c) Sauvola法分割结果;(d) 本文方法分割结果

    Figure 4.  Underwater calibration image and image segmentation results. (a) Underwater calibration image; segmentation results of (b) Ostu method, (c) of Sauvola method and (d) of the proposed method

    图 5  去雾图像

    Figure 5.  Dehazed image

    图 6  图像块分解融合技术路线

    Figure 6.  Technical route of image block decomposition and fusion

    图 7  不同分量融合策略

    Figure 7.  Different component fusion strategies

    图 8  高质量水下标定图像。(a) 灰度分布及理论分布;(b) 灰度累计分布及理论累计分布

    Figure 8.  (a) Grayscale distribution and theoretical distribution and (b) grayscale cumulative distribution and theoretical cumulative distribution of high-quality underwater calibration images.

    图 9  不同数量块处理时间

    Figure 9.  Processing times of different number of image blocks

    图 10  不同数量块增强结果。 (a) 5×5;(b) 10×10;(c) 20×20;(d) 30×30

    Figure 10.  Enhanced results of image blocks with different quantities. (a) 5×5; (b) 10×10; (a) 20×20; (b) 30×30

    图 11  水下相机标定示意图

    Figure 11.  Schematic diagram of underwater camera calibration

    图 12  不同浑浊度下的标定图像及增强后结果。(a)~(d) 7.6NTU, 11.4NTU, 15.7NTU, 18.4NTU不同浑浊度水下标定图像;(e)~(h) MSR增强结果;(i)~(l) UDCP增强结果;(m)~(p) ACDC增强结果;(r)~(u) 本文方法增强结果

    Figure 12.  Underwater calibration images and enhanced results under different turbidities. (a)~(d) tribidity are 7.6 NTU, 11.4 NTU, 15.7 NTU, 18.4NTU; (e)~(h) enhanced results of MSR; (i)~(l) enhanced results of UDCP; (m)~(p) enhanced results of ACDC; (r)~(u) enhanced results of the proposed method

    图 13  图像增强(a)前(b)后标志点检测结果

    Figure 13.  Target point detection results (a) before and (b) after image enhancement

    图 14  不同方法在不同浑浊度下不同位姿标志点检测数量。(a) 7.6NTU;(b) 11.4NTU;(c) 15.7NTU;(d) 18.4NTU

    Figure 14.  Number of detected target points in different postures under different turbidities; (a) 7.6 NTU; (b) 11.4 NTU; (c) 15.7NTU; (d) 18.4 NTU

    表  1  不同算法增强后UISM对比

    Table  1.   Comparison of UISM enhanced by different algorithms

    浑浊度(NTU)原图MSRUDCPACDC本文
    7.60.0580.1540.0680.0690.212
    11.40.0330.1200.0390.0610.197
    15.70.0210.0830.0220.0420.183
    18.40.0150.0630.0150.0350.174
    下载: 导出CSV

    表  2  不同算法增强后UIConM对比

    Table  2.   Comparison of UIConM enhanced by different algorithms

    浑浊度(NTU)原图MSRUDCPACDC本文
    7.60.9150.9200.9440.9390.945
    11.40.9040.9180.9360.9350.944
    15.70.8600.8780.9180.9320.942
    18.40.8310.8580.9080.9250.940
    下载: 导出CSV

    表  3  不同算法图像增强后标志点检测数量增加比例

    Table  3.   The proportion of increase in the number of target point detections after image enhancement by different algorithms

    浑浊度(NTU) MSR UDCP ACDC 本文
    7.6 2.3% 0.7% 0.8% 2.0%
    11.4 2.5% 1.4% 0.9% 2.3%
    15.7 9.0% 0.1% −0.5% 9.3%
    18.4 16.3% 0.4% −1.1% 21.2%
    下载: 导出CSV
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