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QUAN X Q , CHEN X Z , QUAN Y Q , et al . Analysis and research progress of deep-sea optical illumination and imaging system [J ] . Chinese Optics , 2018 , 11 ( 2 ): 153 - 165 . (in Chinese) DOI: 10.3788/co. http://doi.org/10.3788/co. http://www.opticsjournal.net/Journals/joao.htm http://www.opticsjournal.net/Journals/joao.htm

谭克 , 邹丽娜 . 正态分布转换算法在声纳图像处理中的应用 [J ] . 兵工学报 , 2016 , 37 ( 6 ): 1052 - 1057 . DOI: 10.3969/j.issn.1000-1093.2016.06.012 http://doi.org/10.3969/j.issn.1000-1093.2016.06.012 根据声纳图像匹配处理的实时性需求，研究了正态分布转换算法。该方法基于对图像中目标点的概率分布进行匹配，不需要提取图像的几何特征或灰度特征，可有效缩短匹配时间。通过对均值滤波法、中值滤波法、小波去噪法、形态学平滑法4种图像去噪和迭代阈值法、最大熵法、一维Otsu法和二维Otsu法4种分割算法，进行仿真并对比，选出符合实时性处理的方法，即中值滤波法和一维Otsui法。基于美国德州仪器公司的数字信号处理芯片，将图像去噪、分割和匹配算法在硬件上实现，硬件实现结果和仿真结果一致,实现了声纳图像匹配的实时处理。

TAN K , ZOU L N . The Application of normal distribution transformation algorithm in sonar image processing [J ] . Acta Armamentarii , 2016 , 37 ( 6 ): 1052 - 1057 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2016.06.012 http://doi.org/10.3969/j.issn.1000-1093.2016.06.012 For the requirements of the real-time processing of sonar image matching processing, the normal distributions transform (NDT) algorithm and its application in the sonar image processing are studied. The NDT algorithm is based on probability model. The coordinate of target point is calculated instead of the gray value of image, which speeds up the calculation speed of the NDT algorithm. Several methods of image denoising and segmentation are compared to give the results and analysis of each method. Based on TI DSP chip, the NDT image matching algorithm is achieved.

LEE H S , SANG W M , EOM I K . Underwater image enhancement using successive color correction and superpixel dark channel prior [J ] . Symmetry , 2020 , 12 ( 8 ): 1220 . DOI: 10.3390/sym12081220 http://doi.org/10.3390/sym12081220 https://www.mdpi.com/2073-8994/12/8/1220 https://www.mdpi.com/2073-8994/12/8/1220 Underwater images generally suffer from quality degradations, such as low contrast, color cast, blurring, and hazy effect due to light absorption and scattering in the water medium. In applying these images to various vision tasks, single image-based underwater image enhancement has been challenging. Thus, numerous efforts have been made in the field of underwater image restoration. In this paper, we propose a successive color correction method with a minimal reddish artifact and a superpixel-based restoration using a color-balanced underwater image. The proposed successive color correction method comprises an effective underwater white balance based on the standard deviation ratio, followed by a new image normalization. The corrected image based on this color balance algorithm barely produces a reddish artifact. The superpixel-based dark channel prior is exploited to enhance the color-corrected underwater image. We introduce an image-adaptive weight factor using the mean of backscatter lights to estimate the transmission map. We perform intensive experiments for various underwater images and compare the performance of the proposed method with those of 10 state-of-the-art underwater image-enhancement methods. The simulation results show that the proposed enhancement scheme outperforms the existing approaches in terms of both subjective and objective quality.

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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.2010.168 http://doi.org/10.1109/TPAMI.2010.168 In this paper, we propose a simple but effective image prior-dark channel prior to remove haze from a single input image. The dark channel prior is a kind of statistics of outdoor haze-free images. It is based on a key observation-most local patches in outdoor haze-free images contain some pixels whose intensity is very low in at least one color channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high-quality haze-free image. Results on a variety of hazy images demonstrate the power of the proposed prior. Moreover, a high-quality depth map can also be obtained as a byproduct of haze removal.

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SCHECHNER Y Y , KARPEL N . Recovery of underwater visibility and structure by polarization analysis [J ] . IEEE Journal of Oceanic Engineering , 2006 , 30 ( 3 ): 570 - 587 . DOI: 10.1109/JOE.2005.850871 http://doi.org/10.1109/JOE.2005.850871 http://ieeexplore.ieee.org/document/1593804/ http://ieeexplore.ieee.org/document/1593804/

LIU F , ZHANG S C , HAN P L , et al . Depolarization index from Mueller matrix descatters imaging in turbid water [J ] . Chinese Optics Letters , 2022 , 20 ( 2 ): 022601 . DOI: 10.3788/COL http://doi.org/10.3788/COL https://www.researching.cn/EN/JournalMain/col https://www.researching.cn/EN/JournalMain/col

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.24.009826 http://doi.org/10.1364/OE.24.009826 In underwater imaging scenarios, the scattering media could cause severe image degradation due to the backscatter veiling as well as signal attenuation. In this paper, we consider the polarization effect of the object, and propose a method of retrieving the objects radiance based on estimating the polarized-difference image of the target signal. We show with a real-world experiment that by taking into account the polarized-difference image of the target signal additionally, the quality of the underwater image can be effectively enhanced, which is particularly effective in the cases where both the object radiance and the backscatter contribute to the polarization, such as underwater detection of the artifact objects.

WU H D , ZHAO M , LI F Q , et al . Underwater polarization-based single pixel imaging [J ] . Journal of the Society for Information Display , 2020 , 28 ( 2 ): 157 - 163 . DOI: 10.1002/jsid.838 http://doi.org/10.1002/jsid.838 This paper extends single pixel imaging (SPI) to underwater scenario. Backscattering, which is a limiting factor for underwater imaging, is analyzed for the SPI, and the forward model of SPI under backscattering is formulated. Inspired by the polarization-based descattering technique, we propose the two cross-polarization SPI detection scheme to eliminate the backscattering light. The descattered image is reconstructed from the cross-polarization data. Experimentally, we construct the underwater polarization-based single-pixel imaging system. And we test our scheme under different turbidity water. It is shown in experiments that our method can provide a clear image at turbid water of 32FTU, where the classical SPI has been severely contaminated by the backscattering, even under the best the polarization state.

BARUKČIČ I , SUN Z P , LI F B , et al . Underwater image enhancement algorithm based on dark channel prior and underwater imaging model [J ] . MATEC Web of Conferences , 2021 , 336 , 06033 . DOI: 10.1051/matecconf/202133606033 http://doi.org/10.1051/matecconf/202133606033 https://www.matec-conferences.org/10.1051/matecconf/202133606033 https://www.matec-conferences.org/10.1051/matecconf/202133606033 The main reason for the degradation of the underwater image is the light absorption and scattering. The images are captured in the underwater environment often have some problems such as loss of image information, low contrast, and color distortion. In order to solve the above problems, this paper proposes an image enhancement method for the underwater environment. With the help of the underwater imaging model and dark channel prior theory, a new idea of adding transmission correction and color compensation to G and B color channels is proposed. Experimental results show that, compared with the traditional methods, this method has a better effect on the underwater image with less color deviation.

李荣华 , 唐智超 , 朴俊峰 , 等 . 偏振参数最优重构的水下降质图像清晰化方法 [J ] . 红外与激光工程 , 2021 , 50 ( 6 ): 20200 426- 1 .

LI R H , TANG Z C , PIAO J F , et al . Underwater degraded image-sharpening method based on optimal polarization parameter reconstruction [J ] . Infrared and Laser Engineering , 2021 , 50 ( 6 ): 2020 0426- 1 . (in Chinese)