Volume 15 Issue 4
Jul.  2022
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LIU Rong-tao, LIU Jia-hang. Brightness correction and color restoration of seabed image obtained by active optical detection[J]. Chinese Optics, 2022, 15(4): 689-702. doi: 10.37188/CO.2021-0211
Citation: LIU Rong-tao, LIU Jia-hang. Brightness correction and color restoration of seabed image obtained by active optical detection[J]. Chinese Optics, 2022, 15(4): 689-702. doi: 10.37188/CO.2021-0211

Brightness correction and color restoration of seabed image obtained by active optical detection

doi: 10.37188/CO.2021-0211
Funds:  Supported by the China High Resolution Earth Observation System Program (No. 41-Y30F07-9001-20/22); Innovative talent program of Jiangsu (No. JSSCRC2021501)
More Information
  • Corresponding author: jhliu@nuaa.edu.cn
  • Received Date: 06 Dec 2021
  • Rev Recd Date: 10 Jan 2022
  • Available Online: 16 May 2022
  • Active optical imaging detection is an important method for seabed topography and environment detection, which is widely used in ocean exploration. However, due to the attenuation effect of light in seawater, the optical images often suffer uneven illumination, color distortion and low contrast. According to the property of underwater active optical imaging, an underwater image enhancement method based on relative radiometric correction is proposed in this paper. The procedure is divided into brightness compensation and color restoration. In brightness compensation, according to the imaging characteristics and radiation attenuation mechanism of a point light source, the relative radiation correction is used to compensate for the channels of underwater images. This stage eliminates the brightness distortion caused by an uneven light source, varying optical paths and so on. In the color restoration, adaptive compensation and rough color balance are performed first on the red channel. Then, the Retinex model is used to restore colors. The real seabed images are used for experiments. The results show that the enhanced images by the proposed method have uniform brightness and natural look. Compared with the other methods, the results of the proposed method are better overall both subjectively and objectively. At the same time, the method proposed in this paper does not need the properties of light source, camera and others. Only the real detection images themselves are used for correction, and achieve better adaptability.

     

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