Abstract:
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 s...Show MoreMetadata
Abstract:
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.
Published in: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Date of Conference: 15-20 April 2018
Date Added to IEEE Xplore: 13 September 2018
ISBN Information:
Electronic ISSN: 2379-190X