Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation.
2021
Single image reflection separation (SIRS), as a representative blind source
separation task, aims to recover two layers, $\textit{i.e.}$, transmission and
reflection, from one mixed observation, which is challenging due to the highly
ill-posed nature. Existing deep learning based solutions typically restore the
target layers individually, or with some concerns at the end of the output,
barely taking into account the interaction across the two streams/branches. In
order to utilize information more efficiently, this work presents a general yet
simple interactive strategy, namely $\textit{your trash is my treasure}$
(YTMT), for constructing dual-stream decomposition networks. To be specific, we
explicitly enforce the two streams to communicate with each other block-wisely.
Inspired by the additive property between the two components, the interactive
path can be easily built via transferring, instead of discarding, deactivated
information by the ReLU rectifier from one stream to the other. Both ablation
studies and experimental results on widely-used SIRS datasets are conducted to
demonstrate the efficacy of YTMT, and reveal its superiority over other
state-of-the-art alternatives. The implementation is quite simple and our code
is publicly available at
$\href{https://github.com/mingcv/YTMT-Strategy}{\textit{https://github.com/mingcv/YTMT-Strategy}}$.
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