The invention discloses a natural image matting method based on 
deep learning, and the method comprises the following steps: obtaining a matting 
data set, and carrying out the data enhancement; establishing a natural image matting model with an 
encoder-decoder structure; in order to reserve detail information, designing an 
encoder to enable the downsampling multiple to be 4, in order to compensatefor reduction of a 
receptive field caused by downsampling multiple reduction, introducing a cavity 
convolution to expand the 
receptive field, and storing the 
maximum pixel position in the maximum 
pooling operation so as to provide position information for an 
upsampling stage; in order to solve the multi-scale problem, a cavity space 
pyramid module is connected to the top of the 
encoder; designinga global context module in a decoder, wherein the global context module is used for fusing high-level features corresponding to the encoder and the decoder; finally training and testing. According tothe method, more detail information is reserved in the 
feature extraction process, and meanwhile, multi-scale features are associated, so that the model can capture 
global information, model 
processing details and large-area transparent objects are facilitated, and the matting quality is improved.