自适应背景光估计与非局部先验的水下图像复原
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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。
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 by18%compared with the existing algorithm.It can be used in the engineering practice of restoring a single underwater image.
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 by18%compared with the existing algorithm.It can be used in the engineering practice of restoring a single underwater image.
引文
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[2]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)
[3]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 L0sparse priors[J].Opt.Precision Eng.,2017,25(9):2490-2498.(in Chinese)
[4]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)
[5]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.
[6]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.
[7]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)
[8]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 darkchannel model[J].Chinese Journal of Quantum Electronics,2016,33(2):140-147.(in Chinese)
[9]SERIKAWA S,LU H.Underwater image dehazing using joint trilateral filter[J].Computers&Electrical Engineering,2014,40(1):41-50.
[10]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.
[11]GALDRAN A,ALVAREZ-GILA A.Automatic red-channel underwater image restoration[J].Journal of Visual Communication&Image Representation,2015,26(5):132-145.
[12]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)
[13]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.
[14]MCGLAMGERY B L.A computer model for underwater camera systems[J].Proc Spie,1980,208(208):221-231.
[15]JERLOV N G.Marine Optics[M].Elsevier Science,1976.
[16]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.
[17]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)
[18]ELAD M.Retinex by two bilateral filters[C].Proceedings of Conference on Scale-Space Theories in Computer Vision,2005:217-229.
[19]HAUTIERE N,TAREL J P,AUBERT D,et al..Blind contrast enhancement assessment by gradient ratioing at visible edges[J].Image A-nalysis&Stereology,2008,27(2):87-95.
[20]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.
[21]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.