1.大连交通大学 机械工程学院,辽宁 大连 116028
2.大连市先进机器人感知与控制技术创新中心,辽宁 大连 116028
扫 描 看 全 文
李荣华,蔡昌烨,张圣辉等.水下降质图像的偏振参数分区优化复原[J].光学精密工程,2023,31(20):3010-3020.
LI Ronghua,CAI Changye,ZHANG Shenghui,et al.Polarization parameter partition optimization restoration method for underwater degraded image[J].Optics and Precision Engineering,2023,31(20):3010-3020.
李荣华,蔡昌烨,张圣辉等.水下降质图像的偏振参数分区优化复原[J].光学精密工程,2023,31(20):3010-3020. DOI: 10.37188/OPE.20233120.3010.
LI Ronghua,CAI Changye,ZHANG Shenghui,et al.Polarization parameter partition optimization restoration method for underwater degraded image[J].Optics and Precision Engineering,2023,31(20):3010-3020. DOI: 10.37188/OPE.20233120.3010.
水下环境获取图像存在对比度下降、清晰度降低、信息衰减等问题。传统方法是对整幅图像的偏振信息进行估计处理,水下图像中目标具有复杂的偏振特性,导致某些目标区域的复原效果欠佳,发生退化。本文提出了一种水下降质图像偏振参数分区优化复原方法。首先经分块对比度加强和引导滤波对两幅偏振正交图像处理后提取高低偏物体的连通域,根据偏振度图像各像素点的值进行分区,优化了对低、高偏振度目标物区域的提取过程;其次分别估计各目标物的目标光偏振度,解决了传统方法中全局估计中复杂目标的错误估计问题;最后对后向散射光偏振度图像进行迭代优化,得到最优选区。实验表明:本文实验结果的主观视觉质量提升显著,前两组实验在低浑浊度下对比原始光强图,客观评价指标EME值提升平均达554%,对比度则平均提升528%,第三组实验在低照度高浑浊度环境下对比原始光强图,EME值提升达379%,对比度则提升956%。三组实验自然图像质量评价指标NIQE值表现良好,图像更加自然。本文的方法较传统方法能有效地复原水下浑浊图像,增加图像对比度,削弱信息衰减,实现更好的图像清晰化效果。
In real water environments, common imaging problems include contrast reduction, low definition, and information attenuation. The traditional estimation method involves estimating the polarization information of the entire image. In real underwater images, the target has complex polarization characteristics, the restoration effects of some target areas are poor, and even degradation occurs. In this study, a method of polarization parameter partition optimization restoration for water-degraded images was proposed. First, the connected domain of an object with high and low polarizations was extracted after two images were processed with orthogonal polarization by block contrast enhancement and guided filtering. Based on the pixel values in the polarization image, the extraction process of high and low polarization object regions was optimized. Second, the polarization of each object was estimated, which solved the problem of incorrect estimation of complex objects in traditional global estimation methods. Finally, the image of polarization degree of backscattered light was iteratively optimized to obtain the optimal selection. Experimental results show that the subjective visual quality of the image is improved significantly. In two initial experiments, the original light intensity maps under low turbidity are compared. The measurement of enhancement by entropy (EME) value of the objective evaluation index and the contrast increases by 554% and 528% on average, respectively. In a third set of experiments, in which a comparison of the original light intensity maps in an environment of low illumination and high turbidity was conducted, the EME value and contrast are improved by 379% and 956%, respectively. Three sets of natural image quality evaluation (NIQE) indices indicate the proposed method has good performance, and a more natural image is produced. Compared with the traditional method, the proposed method can effectively restore a turbid image, increase image contrast, weaken information attenuation, and achieve a better image sharpening effect.
信息衰减对比度加强分区优化迭代优化图像清晰化
information attenuationincreased contrastpartition optimizationiterative optimizationimage sharpening
YUAN X, GUO L X, LUO C T, et al. A survey of target detection and recognition methods in underwater turbid areas[J]. Applied Sciences, 2022, 12(10):4898. doi: 10.3390/app12104898http://dx.doi.org/10.3390/app12104898
HU K, WENG C, ZHANG Y, et al. An overview of underwater vision enhancement: from traditional methods to recent deep learning[J]. Journal of Marine Science and Engineering. 2022, 10(2): 241. doi: 10.3390/jmse10020241http://dx.doi.org/10.3390/jmse10020241
郭继昌, 李重仪, 郭春乐, 等. 水下图像增强和复原方法研究进展[J]. 中国图象图形学报, 2017(3): 273-287. doi: 10.11834/jig.20170301http://dx.doi.org/10.11834/jig.20170301
GUO J C, LI C Y, GUO C L, et al. Research progress of underwater image enhancement and restoration methods[J]. Journal of Image and Graphics, 2017(3): 273-287.(in Chinese). doi: 10.11834/jig.20170301http://dx.doi.org/10.11834/jig.20170301
赵永强, 戴慧敏, 申凌皓, 等. 水下偏振清晰成像方法综述[J]. 红外与激光工程, 2020, 49(6): 3788/IRLA20190574. doi: 10.3788/irla.4_2019-0574http://dx.doi.org/10.3788/irla.4_2019-0574
ZHAO Y Q, DAI H M, SHEN L H, et al. Review of underwater polarization clear imaging methods[J]. Infrared and Laser Engineering, 2020, 49(6): 3788/IRLA20190574.(in Chinese). doi: 10.3788/irla.4_2019-0574http://dx.doi.org/10.3788/irla.4_2019-0574
万振华, 赵开春, 褚金奎. 基于偏振成像的方位测量误差建模与分析[J]. 光学 精密工程, 2019, 27(8): 1688-1696. doi: 10.3788/ope.20192708.1688http://dx.doi.org/10.3788/ope.20192708.1688
WAN Z H, ZHAO K C, CHU J K. Modeling and analysis of orientation measurement error based on polarization imaging[J]. Opt. Precision Eng., 2019, 27(8): 1688-1696. (in Chinese). doi: 10.3788/ope.20192708.1688http://dx.doi.org/10.3788/ope.20192708.1688
BAZEILLE S, QUIDU I, JAULIN L, et al. Automatic underwater image pre-processing[J]. CMM'06, 2006.
HE K M, SUN J, TANG X O. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12): 2341-2353. doi: 10.1109/tpami.2010.168http://dx.doi.org/10.1109/tpami.2010.168
GALDRAN A, PARDO D, PICÓN A, et al. Automatic Red-Channel underwater image restoration[J]. Journal of Visual Communication and Image Representation, 2015, 26: 132-145. doi: 10.1016/j.jvcir.2014.11.006http://dx.doi.org/10.1016/j.jvcir.2014.11.006
SCHECHNER Y Y, KARPEL N. Recovery of underwater visibility and structure by polarization analysis[J]. IEEE Journal of Oceanic Engineering, 2005, 30(3): 570-587. doi: 10.1109/joe.2005.850871http://dx.doi.org/10.1109/joe.2005.850871
SCHECHNER Y Y, KARPEL N. Clear Underwater Vision[C]. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR. June 27 - July 2, 2004, Washington, DC, USA. IEEE, 2004: I.
HUANG B J, LIU T G, HU H F, et al. Underwater image recovery considering polarization effects of objects[J]. Optics Express, 2016, 24(9): 9826-9838. doi: 10.1364/oe.24.009826http://dx.doi.org/10.1364/oe.24.009826
HU H F, ZHAO L, HUANG B J, et al. Enhancing visibility of polarimetric underwater image by transmittance correction[J]. IEEE Photonics Journal, 2017, 9(3): 1-10. doi: 10.1109/jphot.2017.2698000http://dx.doi.org/10.1109/jphot.2017.2698000
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. doi: 10.3788/ope.20182607.1621http://dx.doi.org/10.3788/ope.20182607.1621
FAN Z G, SONG Q, DAI Q Q, et al. Underwater target polarization recovery method based on global parameter estimation[J]. Opt. Precision Eng., 2018, 26(7): 1621-1632. (in Chinese). doi: 10.3788/ope.20182607.1621http://dx.doi.org/10.3788/ope.20182607.1621
褚金奎, 张培奇, 成昊远, 等. 基于特定偏振态成像的水下图像去散射方法[J]. 光学 精密工程, 2021, 29(5): 1207-1215. doi: 10.37188/OPE.20212905.1207http://dx.doi.org/10.37188/OPE.20212905.1207
CHU J K, ZHANG P Q, CHENG H Y, et al. De-scattering method of underwater image based on imaging of specific polarization state[J]. Opt. Precision Eng., 2021, 29(5): 1207-1215. (in Chinese). doi: 10.37188/OPE.20212905.1207http://dx.doi.org/10.37188/OPE.20212905.1207
LI H X, ZHU J P, DENG J X, et al. Visibility enhancement of underwater images based on polarization common-mode rejection of a highly polarized target signal[J]. Optics Express, 2022, 30(24): 43973. doi: 10.1364/oe.474365http://dx.doi.org/10.1364/oe.474365
ZHANG H J, ZHOU N, MENG Q G, et al. Local optimum underwater polarization imaging enhancement based on connected domain prior[J]. Journal of Optics, 2022, 24(10): 105701. doi: 10.1088/2040-8986/ac83d6http://dx.doi.org/10.1088/2040-8986/ac83d6
LI X B, HU H F, ZHAO L, et al. Polarimetric image recovery method combining histogram stretching for underwater imaging[J]. Scientific Reports, 2018, 8: 12430. doi: 10.1038/s41598-018-30566-8http://dx.doi.org/10.1038/s41598-018-30566-8
YANG M, SOWMYA A. New image quality evaluation metric for underwater video[J]. IEEE Signal Processing Letters, 2014, 21(10): 1215-1219. doi: 10.1109/lsp.2014.2330848http://dx.doi.org/10.1109/lsp.2014.2330848
AGAIAN S S, PANETTA K, GRIGORYAN A M. Transform-based image enhancement algorithms with performance measure[J]. IEEE Transactions on Image Processing, 2001, 10(3): 367-382. doi: 10.1109/83.908502http://dx.doi.org/10.1109/83.908502
0
Views
25
下载量
0
CSCD
Publicity Resources
Related Articles
Related Author
Related Institution