Bust Portraits Matting Based on Improved U-Net

Abstract

Extracting complete portrait foregrounds from natural images is widely used in image editing and high-definition map generation. When making high-definition maps, it is often necessary to matte passers-by to guarantee their privacy. Current matting methods that do not require additional trimap inputs often suffer from inaccurate global predictions or blurred local details. Portrait matting, as a soft segmentation method, allows the creation of excess areas during segmentation, which inevitably leads to noise in the resulting alpha image as well as excess foreground information, so it is not necessary to keep all the excess areas. To overcome the above problems, this paper designed a contour sharpness refining network (CSRN) that modifies the weight of the alpha values of uncertain regions in the prediction map. It is combined with an end-to-end matting network for bust matting based on the U-Net target detection network containing Residual U-blocks. An end-to-end matting network for bust matting is designed. The network can effectively reduce the image noise without affecting the complete foreground information obtained by the deeper network, thus obtaining a more detailed foreground image with fine edge details. The network structure has been tested on the PPM-100, the RealWorldPortrait-636, and a self-built dataset, showing excellent performance in both edge refinement and global prediction for half-figure portraits.

Keywords

• image matting
• bust portraits
• deep learning
• driver monitoring