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We present a novel, blind, single image deblurring method that utilizesinformation regarding blur kernels. Our model solves the deblurring problem bydividing it into two successive tasks: (1) blur kernel estimation and (2) sharpimage restoration. We first introduce a kernel estimation network that producesadaptive blur kernels based on the analysis of the blurred image. The networklearns the blur pattern of the input image and trains to generate theestimation of image-specific blur kernels. Subsequently, we propose adeblurring network that restores sharp images using the estimated blur kernel.To use the kernel efficiently, we propose a kernel-adaptive AE block thatencodes features from both blurred images and blur kernels into a lowdimensional space and then decodes them simultaneously to obtain anappropriately synthesized feature representation. We evaluate our model onREDS, GOPRO and Flickr2K datasets using various Gaussian blur kernels.Experiments show that our model can achieve state-of-the-art results on eachdataset.

Blur Invariant Kernel-Adaptive Network for Single Image Blind deblurring