Single Image Reflection Removal through Cascaded Refinement
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We address the problem of removing undesirable reflections from a single image captured through a glass surface, which is an ill-posed, challenging but practically important problem for photo enhancement. Inspired by iterative structure reduction for hidden community detection in social networks, we propose an Iterative Boost Convolutional LSTM Network (IBCLN) that enables cascaded prediction for reflection removal. IBCLN iteratively refines estimates of the transmission and reflection layers at each step in a manner that they can boost the prediction quality for each other. The intuition is that progressive refinement of the transmission or reflection layer is aided by increasingly better estimates of these quantities as input, and that transmission and reflection are complementary to each other in a single image and thus provide helpful auxiliary information for each other's prediction. To facilitate training over multiple cascade steps, we employ LSTM to address the vanishing gradient problem, and incorporate a reconstruction loss as further training guidance at each step. In addition, we create a dataset of real-world images with reflection and ground-truth transmission layers to mitigate the problem of insufficient data. Through comprehensive experiments, IBCLN demonstrates performance that surpasses state-of-the-art reflection removal methods.
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