Xueli Shen
ABSTRACT
In the underwater environment, the color distortion and low contrast of the image are caused by environmental problems such as light absorption and scattering, which leads to the degradation of image quality. In order to improve the visual effect of the image, this paper proposed a color correction underwater image enhancement algorithm based on convolutional neural network. Firstly, a new underwater imaging model was used to correct the color cast problem of underwater images. Then, the convolutional neural network is used to extract the channel features of the corrected image, and the channel features are re-weighted by the multi-scale attention module to enhance the consistency of different feature maps, and the feature fusion is performed with the color corrected image. Finally, the image enhancement effect was improved by the fusion of features through the reconstruction calculation module. Experimental results show that the proposed algorithm can better correct the color distortion of the image and improve the image contrast. The main advantage is that the running speed of the proposed algorithm is two times faster than other advanced underwater image enhancement methods.
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Index Terms
- Underwater Image Enhancement with Color Correction using Convolutional Neural Networks
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