Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Deep Latent Subspaces for Image Denoising   Heterogeneity exists in most camera images. This heterogeneity manifests
itself across the image space as varied Moire ringing, motion-blur,
color-bleaching or lens based projection distortions. Moreover, combinations of
these image artifacts can be present in small or large pixel neighborhoods,
within an acquired image. Current camera image processing pipelines, including
deep trained versions, tend to rectify the issue applying a single filter that
is homogeneously applied to the entire image. This is also particularly true
when an encoder-decoder type deep architecture is trained for the task. In this
paper, we present a structured deep learning model that solves the
heterogeneous image artifact filtering problem. We call our deep trained model
the Patch Subspace Variational Autoencoder (PS-VAE) for Camera ISP. PS-VAE does
not necessarily assume uniform image distortion levels nor similar artifact
types within the image. Rather, our model attempts to learn to cluster
different patches extracted from images into artifact type and distortion
levels, within multiple latent subspaces (e.g. Moire ringing artifacts are
often a higher dimensional latent distortion than a Gaussian motion blur
artifact). Each image's patches are encoded into soft-clusters in their
appropriate latent sub-space, using a prior mixture model. The decoders of the
PS-VAE are also trained in an unsupervised manner for each of the image patches
in each soft-cluster. Our experimental results demonstrates the flexibility and
performance that one can achieve through improved heterogeneous filtering. We
compare our results to a conventional one-encoder-one-decoder architecture.

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The categories of this paper are: hep-th
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The categories of this paper are: nlin
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The categories of this paper are: gr-qc
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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: q-alg
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The categories of this paper are: chem-ph
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The categories of this paper are: adap-org
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The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
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The categories of this paper are: physics
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The categories of this paper are: nucl-th
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The categories of this paper are: stat
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The categories of this paper are: quant-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat