• Opto-Electronic Advances
  • Vol. 1, Issue 10, 180024 (2018)
[in Chinese]1、2、3、4, [in Chinese]1、2、*, [in Chinese]1、2, [in Chinese]2, and [in Chinese]4
Author Affiliations
  • 1Key Laboratory of Adaptive Optics, Chinese Academy of Sciences, Chengdu 610209, China
  • 2Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China
  • 3University of Chinese Academy of Sciences, Beijing 100049, China
  • 4Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
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    DOI: 10.29026/oea.2018.180024 Cite this Article
    [in Chinese], [in Chinese], [in Chinese], [in Chinese], [in Chinese]. Underwater image enhancement based on red channel weighted compensation and gamma correction model[J]. Opto-Electronic Advances, 2018, 1(10): 180024 Copy Citation Text show less

    Abstract

    Due to the special characteristics of light in water, the information of the red channel is seriously attenuated in collected image. This causes other colors to dominate the image. This paper proposes an underwater image enhancement algorithm based on red channel weighted compensation and gamma correction model. Firstly, by analyzing the attenuation characteristics of RGB channels, the intensity and the edge information of red channel are compensated by weighting the attenuation coefficient ratio between different channels to correct the chromaticity. Then the gamma correction model is employed to stretch the intensity range to enhance the contrast which makes the image look clearer. The experimental results show that the proposed algorithm can correct the color cast effect and improve the contrast by nearly 2 times for the underwater images with too much red component attenuation.
    $ {{t}_{\lambda }}(x)=\exp [-{{\beta }_{\lambda }}d(x)]\;\;, $ (1)

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    $ {{b}_{\lambda }}=(-0.00113\lambda +1.62517)b({{\lambda }_{\text{r}}})\;\;, $ (2)

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    $ \frac{{{c}_{\text{g}}}}{{{c}_{\text{r}}}}=\frac{{{b}_{\text{g}}}{{B}_{\text{r}, \infty }}}{{{b}_{\text{r}}}{{B}_{\text{g}, \infty }}}\ \ , $ (3)

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    $ \frac{{{c}_{\text{b}}}}{{{c}_{\text{r}}}}=\frac{{{b}_{\text{b}}}{{B}_{\text{r}, \infty }}}{{{b}_{\text{r}}}{{B}_{\text{b}, \infty }}}\ \ , $ (4)

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    $ {{R}_{\text{med}}}={{f}_{\text{medfilt}}}(R)\ \ , $ (5)

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    $ {{G}_{\text{med}}}={{f}_{\text{medfilt}}}(G)\ \ , $ (6)

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    $ {{B}_{\text{med}}}={{f}_{\text{medfilt}}}(B)\ \ , $ (7)

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    $ {{\omega }_{\text{r}}}={{(1+\frac{{{c}_{\text{g}}}}{{{c}_{\text{r}}}}+\frac{{{c}_{\text{b}}}}{{{c}_{\text{r}}}})}^{-1}}\ \ , $ (8)

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    $ {\omega _{\rm{g}}} = \frac{{{c_{\rm{g}}}}}{{{c_{\rm{r}}}}} \cdot {(1 + \frac{{{c_{\rm{g}}}}}{{{c_{\rm{r}}}}} + \frac{{{c_{\rm{b}}}}}{{{c_{\rm{r}}}}})^{ - 1}}\;\;, $ (9)

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    $ {\omega _{\rm{b}}} = \frac{{{c_{\rm{b}}}}}{{{c_{\rm{r}}}}} \cdot {(1 + \frac{{{c_{\rm{g}}}}}{{{c_{\rm{r}}}}} + \frac{{{c_{\rm{b}}}}}{{{c_{\rm{r}}}}})^{ - 1}}\;\;, $ (10)

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    $ {R_{{\rm{new}}}} = {\omega _{\rm{r}}} \times R + {\omega _{\rm{g}}} \times G + {\omega _{\rm{b}}} \times B\;\;, $ (11)

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    $ {q_i} = {a_k} \cdot {I_i} + {b_k}, \forall i \in {\omega _k}\;\;, $ (12)

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    $ {I_{{\rm{low}}}} = \{ i|\min (\sum\nolimits_{j = 0}^i {hist(j)} \ge {r_1}), \;0 \le i \le 255\} \;\;, $ (13)

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    $ {I_{{\rm{high}}}} = \{ i|\min (\sum\nolimits_{j = 0}^i {hist(j)} \ge {r_2}), \;0 \le i \le 255\} \;\;, $ (14)

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    $ O(x) = \left\{ {\begin{array}{*{20}{c}} {{O_{{\rm{low}}}}\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;{\rm{, }}I(x) \le {I_{{\rm{low}}}}}\\ {{O_{{\rm{low}}}} + ({O_{{\rm{high}}}} - {O_{{\rm{low}}}}) \times {{(\frac{{I(x) - {I_{{\rm{low}}}}}}{{{I_{{\rm{high}}}} - {I_{{\rm{low}}}}}})}^\gamma }\;{\rm{, }}{I_{{\rm{low}}}} < I(x) \le {I_{{\rm{high}}}}}\\ {{O_{{\rm{high}}}}\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;{\rm{, }}{I_{{\rm{high}}}} < I(x)} \end{array}} \right.. $ (15)

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    [in Chinese], [in Chinese], [in Chinese], [in Chinese], [in Chinese]. Underwater image enhancement based on red channel weighted compensation and gamma correction model[J]. Opto-Electronic Advances, 2018, 1(10): 180024
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