Blind Motion Deblurring with Pixel-Wise Kernel Estimation via Kernel
  Prediction Networks
Guillermo Carbajal Patricia Vitoria José Lezama Pablo Musé
In recent years, the removal of motion blur in photographs has seen
impressive progress in the hands of deep learning-based methods, trained to map
directly from blurry to sharp images. For this reason, approaches that
explicitly use a forward degradation model received significantly less
attention. However, a well-defined specification of the blur genesis, as an
intermediate step, promotes the generalization and explainability of the
method. Towards this goal, we propose a learning-based motion deblurring method
based on dense non-uniform motion blur estimation followed by a non-blind
deconvolution approach. Specifically, given a blurry image, a first network
estimates the dense per-pixel motion blur kernels using a lightweight
representation composed of a set of image-adaptive basis motion kernels and the
corresponding mixing coefficients. Then, a second network trained jointly with
the first one, unrolls a non-blind deconvolution method using the motion kernel
field estimated by the first network. The model-driven aspect is further
promoted by training the networks on sharp/blurry pairs synthesized according
to a convolution-based, non-uniform motion blur degradation model. Qualitative
and quantitative evaluation shows that the kernel prediction network produces
accurate motion blur estimates, and that the deblurring pipeline leads to
restorations of real blurred images that are competitive or superior to those
obtained with existing end-to-end deep learning-based methods. Code and trained
models are available at https://github.com/GuillermoCarbajal/J-MKPD/.