An underwater image enhancement method combining deep learning and multi-scale orientation filter Retinex is proposed to tackle the problems of blurry texture and serious color distortion.Firstly,the land image is degraded by texture and histogram matching method to establish a dataset which simulates the underwater image distortion,and an end-to-end convolutional neural network (CNN) model is built.By using the model,color correction is performed on original underwater images to obtain color-restored underwater images.Then,the multi-scale Retinex (MSR) method is used for the brightness channel of the color restoration images to generate texture-enhanced images.Finally,chrominance of the color-restored images and the texture-enhanced images are fused to eventually get the enhanced underwater images.The proposed method is tested on the simulated underwater image dataset and real underwater images individually.The experimental results show that root mean square error, peak signal-to-noise ratio,CIEDE2000,and underwater image quality measurement are 0.302 0,17.239 2 dB,16.878 4 and 4.960 0 and prevail to five comparison methods. The enhanced underwater images are more real and natural.In conclusion,the proposed method can effectively improve the clarity and contrast while accurately correcting the color distortion of the underwater images.