  Image deconvolution is the process of recovering convolutional degraded
images, which is always a hard inverse problem because of its mathematically
ill-posed property. On the success of the recently proposed deep image prior
(DIP), we build an image deconvolution model with deep image and kernel priors
(DIKP). DIP is a learning-free representation which uses neural net structures
to express image prior information, and it showed great success in many
energy-based models, e.g. denoising, super-resolution, inpainting. Instead, our
DIKP model uses such priors in image deconvolution to model not only images but
also kernels, combining the ideas of traditional learning-free deconvolution
methods with neural nets. In this paper, we show that DIKP improve the
performance of learning-free image deconvolution, and we experimentally
demonstrate this on the standard benchmark of six standard test images in terms
of PSNR and visual effects.
