A Deep Variational Bayesian Framework for Blind Image Deblurring
- URL: http://arxiv.org/abs/2106.02884v1
- Date: Sat, 5 Jun 2021 12:47:36 GMT
- Title: A Deep Variational Bayesian Framework for Blind Image Deblurring
- Authors: Hui Wang, Zongsheng Yue, Qian Zhao, Deyu Meng
- Abstract summary: Blind image deblurring is an important yet very challenging problem in low-level vision.
We present a deep variational Bayesian framework for blind image deblurring.
- Score: 46.585763459441154
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Blind image deblurring is an important yet very challenging problem in
low-level vision. Traditional optimization based methods generally formulate
this task as a maximum-a-posteriori estimation or variational inference
problem, whose performance highly relies on the handcraft priors for both the
latent image and the blur kernel. In contrast, recent deep learning methods
generally learn, from a large collection of training images, deep neural
networks (DNNs) directly mapping the blurry image to the clean one or to the
blur kernel, paying less attention to the physical degradation process of the
blurry image. In this paper, we present a deep variational Bayesian framework
for blind image deblurring. Under this framework, the posterior of the latent
clean image and blur kernel can be jointly estimated in an amortized inference
fashion with DNNs, and the involved inference DNNs can be trained by fully
considering the physical blur model, together with the supervision of data
driven priors for the clean image and blur kernel, which is naturally led to by
the evidence lower bound objective. Comprehensive experiments are conducted to
substantiate the effectiveness of the proposed framework. The results show that
it can not only achieve a promising performance with relatively simple
networks, but also enhance the performance of existing DNNs for deblurring.
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