  We present a novel, blind, single image deblurring method that utilizes
information regarding blur kernels. Our model solves the deblurring problem by
dividing it into two successive tasks: (1) blur kernel estimation and (2) sharp
image restoration. We first introduce a kernel estimation network that produces
adaptive blur kernels based on the analysis of the blurred image. The network
learns the blur pattern of the input image and trains to generate the
estimation of image-specific blur kernels. Subsequently, we propose a
deblurring network that restores sharp images using the estimated blur kernel.
To use the kernel efficiently, we propose a kernel-adaptive AE block that
encodes features from both blurred images and blur kernels into a low
dimensional space and then decodes them simultaneously to obtain an
appropriately synthesized feature representation. We evaluate our model on
REDS, GOPRO and Flickr2K datasets using various Gaussian blur kernels.
Experiments show that our model can achieve state-of-the-art results on each
dataset.
