  In this paper, we consider the problem in defocus image deblurring. Previous
classical methods follow two-steps approaches, i.e., first defocus map
estimation and then the non-blind deblurring. In the era of deep learning, some
researchers have tried to address these two problems by CNN. However, the
simple concatenation of defocus map, which represents the blur level, leads to
suboptimal performance. Considering the spatial variant property of the defocus
blur and the blur level indicated in the defocus map, we employ the defocus map
as conditional guidance to adjust the features from the input blurring images
instead of simple concatenation. Then we propose a simple but effective network
with spatial modulation based on the defocus map. To achieve this, we design a
network consisting of three sub-networks, including the defocus map estimation
network, a condition network that encodes the defocus map into condition
features, and the defocus deblurring network that performs spatially dynamic
modulation based on the condition features. Moreover, the spatially dynamic
modulation is based on an affine transform function to adjust the features from
the input blurry images. Experimental results show that our method can achieve
better quantitative and qualitative evaluation performance than the existing
state-of-the-art methods on the commonly used public test datasets.

Computer Vision and Pattern Recognition;Image and Video Processing