Learning Super-Resolution of Environment Matting of Transparent Objects From a Single Image

Abstract

This paper addresses the problem of super-resolution of environment matting of transparent objects. In contrast to traditional methods of environment matting of transparent objects, which often require a large number of input images or complex camera setups, recent approaches using convolutional neural networks are more practical. In particular, after training, they can generate the environment mattes using a single image. However, they still do not have super-resolution capabilities. This paper first proposes an encoder-decoder network with restoration units for super-resolution environment matting, called Enhanced Transparent Object Matting Network (ETOM-Net). Then, we introduce a refinement phase to improve the details of the output further. The ETOM-Net effectively recovers lost features in the LR input images and produces visually plausible HR environment mattes and the corresponding reconstructed images, demonstrating our method's effectiveness.