Underwater image enhancement has attracted attention for its importance in marine survey and underwater robotics. In the past few years，many underwater image enhancement algorithms have been proposed. The existing deep learning methods ignore the pre-processing process of underwater image and the enhancement of red channel information or weaken this process，resulting in insignificant enhancement results，which are often adapted to specific scenes only and lack the generalization capability. To this end，we establish a brand new underwater image enhancement algorithm based on convolutional neural network. In order to make full use of the channel information of feature maps，convolutional cores of different dimensions are used to obtain more number of channels in the same dimensions. Then，we construct an attention mechanism based on red channels to enhance the extraction of features for red channels that tend to lose information in images. Finally，we carry out ablation experiments in the EUVP， UFO120 dataset to demonstrate the effectiveness of the red channel attention mechanism. By analyzing the various indicators of the contrast experiment enhancement results，we prove that the enhancement results have higher structural similarity and peak signal-to-noise ratio，and have higher color balance，clarity and contrast in terms of reference-free indicators，and its comprehensive performance is better than the previous methods.
