Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to See Through Obstructions with Layered Decomposition   We present a learning-based approach for removing unwanted obstructions, such
as window reflections, fence occlusions, or adherent raindrops, from a short
sequence of images captured by a moving camera. Our method leverages motion
differences between the background and obstructing elements to recover both
layers. Specifically, we alternate between estimating dense optical flow fields
of the two layers and reconstructing each layer from the flow-warped images via
a deep convolutional neural network. This learning-based layer reconstruction
module facilitates accommodating potential errors in the flow estimation and
brittle assumptions, such as brightness consistency. We show that the proposed
approach learned from synthetically generated data performs well to real
images. Experimental results on numerous challenging scenarios of reflection
and fence removal demonstrate the effectiveness of the proposed method.

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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: adap-org
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The categories of this paper are: math-ph
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The categories of this paper are: econ
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The categories of this paper are: stat
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The categories of this paper are: nucl-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: astro-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph
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The categories of this paper are: funct-an
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The categories of this paper are: mtrl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: patt-sol