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Contextual-based Image Inpainting: Infer, Match, and Translate
We study the task of image inpainting, which is to fill in the missing region
of an incomplete image with plausible contents. To this end, we propose a
learning-based approach to generate visually coherent completion given a
high-resolution image with missing components. In order to overcome the
difficulty to directly learn the distribution of high-dimensional image data,
we divide the task into inference and translation as two separate steps and
model each step with a deep neural network. We also use simple heuristics to
guide the propagation of local textures from the boundary to the hole. We show
that, by using such techniques, inpainting reduces to the problem of learning
two image-feature translation functions in much smaller space and hence easier
to train. We evaluate our method on several public datasets and show that we
generate results of better visual quality than previous state-of-the-art
methods.Comment: ECCV 2018 camera read
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