Color Correction Underwater Image Enhancement Method Based on Convolutional Neural Network
In the underwater environment,due to environmental problems such as light absorption and scattering,the color distortion and contrast of the image are low,resulting in a decrease in image quality.In order to improve the visual effect of the image,an underwater image enhancement algorithm based on convolutional neural network is proposed.Firstly,a new underwater imaging model is used to correct the color deviation of underwater images.Then,the convolutional neural network is used to extract the corrected image channel features,and the channel features are re-weighted by the multi-scale attention module to enhance the consistency of different feature maps,and feature fusion is performed with the color-corrected image.Finally,the feature is fused by the reconstruction calculation module to improve the image enhancement effect.The experimental results show that the proposed algorithm can better correct the image color distortion and improve the image contrast.The main advantage is that the running speed of the proposed algorithm is twice faster than that of other advanced underwater image enhancement methods.
万方数据
维普
