  Cutting out an object and estimating its opacity mask, known as image
matting, is a key task in many image editing applications. Deep learning
approaches have made significant progress by adapting the encoder-decoder
architecture of segmentation networks. However, most of the existing networks
only predict the alpha matte and post-processing methods must then be used to
recover the original foreground and background colours in the transparent
regions. Recently, two methods have shown improved results by also estimating
the foreground colours, but at a significant computational and memory cost.
  In this paper, we propose a low-cost modification to alpha matting networks
to also predict the foreground and background colours. We study variations of
the training regime and explore a wide range of existing and novel loss
functions for the joint prediction.
  Our method achieves the state of the art performance on the Adobe
Composition-1k dataset for alpha matte and composite colour quality. It is also
the current best performing method on the alphamatting.com online evaluation.
