  In recent years, deep natural image matting has been rapidly evolved by
extracting high-level contextual features into the model. However, most current
methods still have difficulties with handling tiny details, like hairs or furs.
In this paper, we argue that recovering these microscopic details relies on
low-level but high-definition texture features. However, {these features are
downsampled in a very early stage in current encoder-decoder-based models,
resulting in the loss of microscopic details}. To address this issue, we design
a deep image matting model {to enhance fine-grained details. Our model consists
of} two parallel paths: a conventional encoder-decoder Semantic Path and an
independent downsampling-free Textural Compensate Path (TCP). The TCP is
proposed to extract fine-grained details such as lines and edges in the
original image size, which greatly enhances the fineness of prediction.
Meanwhile, to leverage the benefits of high-level context, we propose a feature
fusion unit(FFU) to fuse multi-scale features from the semantic path and inject
them into the TCP. In addition, we have observed that poorly annotated trimaps
severely affect the performance of the model. Thus we further propose a novel
term in loss function and a trimap generation method to improve our model's
robustness to the trimaps. The experiments show that our method outperforms
previous start-of-the-art methods on the Composition-1k dataset.
