Alpha matting for portraits using encoder-decoder models

Image matting is a technique used to extract the foreground and background from a given image. In the past, classical algorithms based on sampling, propagation, or a combination of the two were used to perform image matting; however, most of these have produced poor results when applied to images with complex backgrounds. They are also unable to extract with high accuracy foreground images that are comprised of thin objects. In this context, the use of deep learning to solve the image matting problem has gained increasing popularity. In this paper, an encoder-decoder model for alpha matting of human portraits using deep learning is proposed. The model used comprises two parts: the first is an encoder-decoder model, which is a deep convolutional network that has 11 convolutional layers and 5 max-pooling layers in the encoder stage and 11 convolutional layers and 5 unpooling layers in the decoder stage. This portion of the model takes the image and trimap as input produces the coarse alpha matte as the output. The second part is the refinement stage with four convolutional layers, responsible for further refining the coarse alpha matte that was produced by the encoder-decoder stage to obtain an alpha matte of high accuracy. The model was trained using 43,100 images. When tested using the alphamatting.com dataset, our model’s output was comparable to the industry standard, yielding an average MSE of 0.023 and an average SAD loss of 66.5.