  Dynamic scene deblurring is a challenging problem in computer vision. It is
difficult to accurately estimate the spatially varying blur kernel by
traditional methods. Data-driven-based methods usually employ kernel-free
end-to-end mapping schemes, which are apt to overlook the kernel estimation. To
address this issue, we propose a blur-attention module to dynamically capture
the spatially varying features of non-uniform blurred images. The module
consists of a DenseBlock unit and a spatial attention unit with multi-pooling
feature fusion, which can effectively extract complex spatially varying blur
features. We design a multi-level residual connection structure to connect
multiple blur-attention modules to form a blur-attention network. By
introducing the blur-attention network into a conditional generation
adversarial framework, we propose an end-to-end blind motion deblurring method,
namely Blur-Attention-GAN (BAG), for a single image. Our method can adaptively
select the weights of the extracted features according to the spatially varying
blur features, and dynamically restore the images. Experimental results show
that the deblurring capability of our method achieved outstanding objective
performance in terms of PSNR, SSIM, and subjective visual quality. Furthermore,
by visualizing the features extracted by the blur-attention module,
comprehensive discussions are provided on its effectiveness.
