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キーワード:
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要旨:
Many computer vision and computational photography applications
essentially solve an image enhancement problem. The image has been
deteriorated by a specific noise process, such as aberrations from camera
optics and compression artifacts, that we would like to remove. We
describe a framework for learning-based image enhancement. At the core of
our algorithm lies a generic regularization framework that comprises a
prior on natural images, as well as an application-specific conditional
model based on Gaussian processes. In contrast to prior learning-based
approaches, our algorithm can instantly learn task-specific degradation
models from sample images which enables users to easily adapt the
algorithm to a specific problem and data set of interest. This is
facilitated by our efficient approximation scheme of large-scale Gaussian
processes. We demonstrate the efficiency and effectiveness of our approach
by applying it to example enhancement applications including single-image
super-resolution, as well as artifact removal in JPEG- and JPEG
2000-encoded images.
