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.