Image Restoration with Deep Generative Models

Many image restoration problems are ill-posed in nature, hence, beyond the input image, most existing methods rely on a carefully engineered image prior, which enforces some local image consistency in the recovered image. How tightly the prior assumptions are fulfilled has a big impact on the result...

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Bibliographic Details
Published in2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 6772 - 6776
Main Authors Yeh, Raymond A., Lim, Teck Yian, Chen, Chen, Schwing, Alexander G., Hasegawa-Johnson, Mark, Do, Minh N.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2018
Subjects
deep generative models
Gallium nitride
generative adversarial networks
Generators
Image resolution
Image restoration
Noise reduction
Quantization (signal)
Task analysis
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Summary:Many image restoration problems are ill-posed in nature, hence, beyond the input image, most existing methods rely on a carefully engineered image prior, which enforces some local image consistency in the recovered image. How tightly the prior assumptions are fulfilled has a big impact on the resulting task performance. To obtain more flexibility, in this work, we proposed to design the image prior in a data-driven manner. Instead of explicitly defining the prior, we learn it using deep generative models. We demonstrate that this learned prior can be applied to many image restoration problems using an unified framework.
ISSN:2379-190X
DOI:10.1109/ICASSP.2018.8462317