自适应背景光估计与非局部先验的水下图像复原

王一斌; 尹诗白; 吕卓纹

doi:10.3788/OPE.20192702.0499

您当前的位置：

首页 >

文章列表页 >

自适应背景光估计与非局部先验的水下图像复原

信息科学 | 更新时间：2020-08-13

- 自适应背景光估计与非局部先验的水下图像复原
- Underwater image restoration with adaptive background light estimation and non-local prior
- 光学精密工程 2019年27卷第2期页码：499-510
- 作者机构：
  
  1.四川师范大学工学院，四川成都 610101
  2.西南财经大学经济信息工程学院，四川成都 610074
  3.西南财经大学金融智能与金融工程四川省重点实验室，四川成都 610074
  4.西南财经大学互联网金融创新及监管四川省协同创新中心，四川成都 610074
- 作者简介：
  
  [ "王一斌(1982-), 男, 河南人, 博士, 讲师, 研究生导师, 2006年、2009年、2016年于西北工业大学分别获得学士、硕士、博士学位, 主要从模式识别和机器视觉方面的研究。E-mail:yibeen.wong@gmail.com" ]
  [ "尹诗白(1984-), 女, 湖北潜江人, 博士, 副教授, 研究生导师, 2006年、2009年于西安工业大学分别获得学士和硕士学位, 2013年于长安大学获得博士学位, 主要从事图像处理, 机器视觉方面的研究。E-mail: shibai.yin@gmail.com" ]
- 基金信息：
  
  四川省教育厅一般项目资助(18ZB0484);四川师范大学自制仪器设备项目(ZZYQ2017001);国家自然科学基金青年基金资助项目(61502396);西南财经大学中央高校基本科研业务费专项资金项目(JBK150503);西南财经大学中央高校基本科研业务费青年教师成长项目(JBK1801076)
- DOI：10.3788/OPE.20192702.0499
  中图分类号： TP391
- 收稿日期：2018-08-16，
  
  录用日期：2018-10-13，
  
  纸质出版日期：2019-02-15
- 稿件说明：
移动端阅览
王一斌, 尹诗白, 吕卓纹. 自适应背景光估计与非局部先验的水下图像复原[J]. 光学精密工程, 2019,27(2):499-510.

Yi-bin WANG, Shi-bai YIN, Zhuo-wen LÜ. Underwater image restoration with adaptive background light estimation and non-local prior[J]. Optics and precision engineering, 2019, 27(2): 499-510.
王一斌, 尹诗白, 吕卓纹. 自适应背景光估计与非局部先验的水下图像复原[J]. 光学精密工程, 2019,27(2):499-510. DOI： 10.3788/OPE.20192702.0499.

Yi-bin WANG, Shi-bai YIN, Zhuo-wen LÜ. Underwater image restoration with adaptive background light estimation and non-local prior[J]. Optics and precision engineering, 2019, 27(2): 499-510. DOI： 10.3788/OPE.20192702.0499.

摘要

有效地实现单幅水下降质图像复原对水下资源探索及环境监控领域的清晰图像获取具有极其重要的意义。为解决常用暗通道先验方法来复原图像时，背景光的估计易受白色物体干扰，且无法有效估计前景中白色物体透射率，复原质量不高的问题。本文提出了自适应背景光估计与非局部先验的水下图像复原算法。首先根据背景光具有高亮度及平坦性的特点，利用阈值分割算法获得背景光的候选区域，再通过图像的色调信息从候选信息中选取最佳的背景光点。随后，利用各颜色通道光的波长与散射系数的相关性，提出了适用于水下图像的非局部先验，并利用该先验估计各通道的透射率。最后针对复原结果中，因水下介质，微生物，水流影响而产生的加性噪声，设计去噪的最小优化问题，并利用引导滤波求解该问题，以去除复原结果中的加性噪声。实验表明：该算法在确保运行效率的基础上，准确地估计透射率，较常用算法的复原精度提高了约18%。证明了该算法能有效用于单幅水下图像复原的工程实践中。

Abstract

It is significant to realize effective single underwater image restoration for acquiring clear image in underwater exploration and underwater environment monitoring field. Most existing algorithms use dark channel priors to restore images

which lead to inaccurate estimates of the background light and transmission map. Hence

a novel method with adaptive background light estimation and nonlocal prior was proposed. Firstly

the candidate water light regions could be obtained by a threshold segmentation algorithm owing to the fact that water light regions have the properties of flat and high brightness. Then

the water light value could be decided from the candidate regions by the dominant tone of the input image. Secondly

the nonlocal prior was built to estimate the transmission map by taking into account the wavelength dependence of the attenuation. Finally

in order to remove the additive noise from the medium and microorganisms

a minimal optimization problem with the solution strategy of guided filter was proposed for obtaining the de-noising result. The experimental results verify that the proposed algorithm not only ensures operation efficiency

but can also estimate the correct transmission map. In general

the restoration precision has improved by 18% compared with the existing algorithm. It can be used in the engineering practice of restoring a single underwater image.

关键词

Keywords

references

杨萍, 郭乙陆, 魏贺, 等.一种用于水下图像的光谱重构方法:理论及应用[J].红外与激光工程, 2017, 46(3): 323001-0323001(8).

YANG P, GUO Y L, WEI H, et al.. Method for spectral restoration of underwater images: theory and application [J]. Infrared & Laser Engineering , 2017, 46(3): 323001-0323001(8). (in Chinese)

范之国, 宋强, 代晴晴, 等.全局参数估计的水下目标偏振复原方法[J].光学精密工程, 2018, 26(7): 1621-1632.

FAN ZH G, SONG Q, DAI Q Q, et al.. Underwater target polarization recovery method based on global parameter estimation [J]. Optics & Precision Engineering , 2018, 26(7): 1621-1632. (in Chinese)

仇翔, 戴明.基于L0稀疏先验的相机抖动模糊图像盲复原[J].光学精密工程, 2017, 25(9):2490-2498.

QIU X, DAI M. Blind restoration of camera shake blurred image based on L0 sparse priors [J]. Opt. Precision Eng ., 2017, 25(9):2490-2498. (in Chinese)

谢昊伶, 彭国华, 王凡, 等.基于背景光估计与暗通道先验的水下图像复原[J].光学学报, 2018, 38(1):0101002.

XIE H L, PENG G H, WANG F, et al .. Underwater image restoration based on background light estimation and dark channel prior [J]. Acta Optica Sinica , 2018, 38(1):0101002. (in Chinese)

DREWS P L, NASCIMENTO E R, BOTELHO S S, et al .. Underwater depth estimation and image restoration based on single images [J]. IEEE Computer Graphics & Applications , 2016, 36(2): 24-35.

HE K, SUN J, TANG X. Single image haze removal using dark channel prior [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence , 2011, 33(12): 2341-2353.

杨爱萍, 郑佳, 王建, 等.基于颜色失真去除与暗通道先验的水下图像复原[J].电子与信息学报, 2015, 37(11): 2541-2547.

YANG A P, ZHENG J, WANG J, et al .. Underwater image restoration based on color cast removal and dark channel prior [J]. Journal of Electronics & Information Technology , 2015, 37(11): 2541-2547. (in Chinese)

曹美, 盛惠兴, 李庆武, 等.基于暗原色先验模型的水下彩色图像增强算法[J].量子电子学报, 2016, 33(2):140-147.

CAO M, SHENG H X, LI Q W, et al .. Underwater color image enhancement algorithm based on prior dark-channel model [J]. Chinese Journal of Quantum Electronics , 2016, 33(2):140-147. (in Chinese)

SERIKAWA S, LU H. Underwater image dehazing using joint trilateral filter [J]. Computers & Electrical Engineering , 2014, 40(1): 41-50.

CHIANG J Y, CHEN Y C. Underwater image enhancement by wavelength compensation and dehazing [J]. IEEE Transactions on Image Processing , 2012, 21(4): 1756-1769.

GALDRAN A, ALVAREZ-GILA A. Automatic red-channel underwater image restoration [J]. Journal of Visual Communication & Image Representation , 2015, 26(5): 132-145.

孙军平, 杨军, 林建恒, 等.船舶水下辐射噪声信号理论模型及仿真[J].物理学报, 2016, 65(12): 147-156.

SUN J P, YANG J, LIN J H, et al .. Theoretical model and simulation of ship underwater radiated noise [J]. Acta Physica Sinica , 2016, 65(12): 147-156. (in Chinese)

BERMAN D, TREIBITZ T, AVIDAN S. Non-local image dehazing [C]. Proceedings of Conference: the IEEE Conference on Computer Vision and Pattern Recognition , 2016: 1674-1682.

MCGLAMGERY B L. A computer model for underwater camera systems [J]. Proc Spie , 1980, 208(208): 221-231.

JERLOV N G. Marine Optics[M]. Elsevier Science, 1976.

AUSTIN R W, PETZOLD T J. Spectral dependence of the diffuse attenuation coefficient of light in ocean waters: a reexamination using new data [C]. Proceedings of Conference on the Ocean Optics X , 1990: 164-165. https://www.researchgate.net/publication/247612577_Spectral_Dependence_Of_The_Diffuse_Attenuation_Coefficient_Of_Light_In_Ocean_Waters

张谢华, 张申, 方帅, 等.煤矿智能视频监控中雾尘图像的清晰化研究[J].煤炭学报, 2014, 39(1): 198-204.

ZHANG X H, ZHANG SH, FANG SH, et al .. Clearing research on fog and dust image in coalmine intelligent video surveillance [J]. Journal of China Coal Society , 2014, 39(1): 198-204. (in Chinese)

ELAD M. Retinex by two bilateral filters [C]. Proceedings of Conference on Scale-Space Theories in Computer Vision , 2005: 217-229. https://link.springer.com/chapter/10.1007%2F11408031_19

HAUTIERE N, TAREL J P, AUBERT D, et al .. Blind contrast enhancement assessment by gradient ratioing at visible edges [J]. Image Analysis & Stereology , 2008, 27(2): 87-95.

CHEN Z, JIANG T, TIAN Y. Quality assessment for comparing image enhancement algorithms [C]. Proceedings of Conference: the IEEE Conference on Computer Vision and Pattern Recognition , 2014: 3003-3010. https://www.researchgate.net/publication/286813580_Quality_Assessment_for_Comparing_Image_Enhancement_Algorithms

DING X, WANG Y, ZHANG J. Underwater image dehaze using scene depth estimation with adaptive color correction [C]. Proceedings of Conference: the IEEE Conference on Oceans , 2017: 978-988.

浏览量

180

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

电力巡检中的偏振图像特征融合

基于机器视觉的透明软管内微量液体体积测量

基于圆卷积神经网络的粘连导电粒子检测

融合多尺度特征的蜗杆表面缺陷检测

相位测量的不连续位置自适应分割