  Blind image deconvolution (BID) is a classic yet challenging problem in the
field of image processing. Recent advances in deep image prior (DIP) have
motivated a series of DIP-based approaches, demonstrating remarkable success in
BID. However, due to the high non-convexity of the inherent optimization
process, these methods are notorious for their sensitivity to the initialized
kernel. To alleviate this issue and further improve their performance, we
propose a new framework for BID that better considers the prior modeling and
the initialization for blur kernels, leveraging a deep generative model. The
proposed approach pre-trains a generative adversarial network-based kernel
generator that aptly characterizes the kernel priors and a kernel initializer
that facilitates a well-informed initialization for the blur kernel through
latent space encoding. With the pre-trained kernel generator and initializer,
one can obtain a high-quality initialization of the blur kernel, and enable
optimization within a compact latent kernel manifold. Such a framework results
in an evident performance improvement over existing DIP-based BID methods.
Extensive experiments on different datasets demonstrate the effectiveness of
the proposed method.
