Light polarization imaging recovery technique in marine fish observations

Recently, the research and exploration of the ocean has attracted the attention of a large number of scholars. Due to the light scattering and absorption in underwater imaging, the fish observation images cause imaging blurred and details lost. To handle the above limitation, an underwater polarization imaging recovery technique is proposed to recover visibility restoration and color correction of images. First, in the CIE-Lab model, morphological filtering is used to repair the missing details of the L channel, and color correction is performed on A channel. Secondly, in the RGB model, adaptive contrast stretching is performed to enhance the contrast of the image. To further improve the image clarity, dark channel dehazing is implemented to reduce the atomization caused by scattering. Extensive experiments demonstrate that the method can significantly enhance the visual effect, clarity, and detail information of the fish image. The restoration and division of the contour information of fish in the image are improved.

[1]  Y.Y. Schechner,et al.  Recovery of underwater visibility and structure by polarization analysis , 2005, IEEE Journal of Oceanic Engineering.

[2]  Silvia Silva da Costa Botelho,et al.  Transmission Estimation in Underwater Single Images , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[3]  Adrian Galdran,et al.  Automatic Red-Channel underwater image restoration , 2015, J. Vis. Commun. Image Represent..

[4]  B. L. McGlamery,et al.  A Computer Model For Underwater Camera Systems , 1980, Other Conferences.

[5]  Chen Gao,et al.  Human-Visual-System-Inspired Underwater Image Quality Measures , 2016, IEEE Journal of Oceanic Engineering.

[6]  Shai Avidan,et al.  Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Mohinder Malhotra Single Image Haze Removal Using Dark Channel Prior , 2016 .

[8]  Ke Hu,et al.  An In-Depth Survey of Underwater Image Enhancement and Restoration , 2019, IEEE Access.

[9]  Mateu Sbert,et al.  Color Channel Compensation (3C): A Fundamental Pre-Processing Step for Image Enhancement , 2019, IEEE Transactions on Image Processing.

[10]  Jules S. Jaffe,et al.  Computer modeling and the design of optimal underwater imaging systems , 1990 .

[11]  Arcot Sowmya,et al.  An Underwater Color Image Quality Evaluation Metric , 2015, IEEE Transactions on Image Processing.

[12]  Jian Sun,et al.  Guided Image Filtering , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  C CosmanPamela,et al.  Underwater Image Restoration Based on Image Blurriness and Light Absorption , 2017 .

[14]  Pamela C. Cosman,et al.  Underwater Image Restoration Based on Image Blurriness and Light Absorption , 2017, IEEE Transactions on Image Processing.

[15]  Ric,et al.  BLIND CONTRAST ENHANCEMENT ASSESSMENT BY GRADIENT RATIOING AT VISIBLE EDGES , 2008 .