  Binary segmentation is used to distinguish objects of interest from
background, and is an active area of convolutional encoder-decoder network
research. The current decoders are designed for specific objects based on the
common backbones as the encoders, but cannot deal with complex backgrounds.
Inspired by the way human eyes detect objects of interest, a new unified
dual-branch decoder paradigm named the difference-aware decoder is proposed in
this paper to explore the difference between the foreground and the background
and separate the objects of interest in optical images. The difference-aware
decoder imitates the human eye in three stages using the multi-level features
output by the encoder. In Stage A, the first branch decoder of the
difference-aware decoder is used to obtain a guide map. The highest-level
features are enhanced with a novel field expansion module and a dual residual
attention module, and are combined with the lowest-level features to obtain the
guide map. In Stage B, the other branch decoder adopts a middle feature fusion
module to make trade-offs between textural details and semantic information and
generate background-aware features. In Stage C, the proposed difference-aware
extractor, consisting of a difference guidance model and a difference
enhancement module, fuses the guide map from Stage A and the background-aware
features from Stage B, to enlarge the differences between the foreground and
the background and output a final detection result. The results demonstrate
that the difference-aware decoder can achieve a higher accuracy than the other
state-of-the-art binary segmentation methods for these tasks.
