  Image deblurring is a classical computer vision problem that aims to recover
a sharp image from a blurred image. To solve this problem, existing methods
apply the Encode-Decode architecture to design the complex networks to make a
good performance. However, most of these methods use repeated up-sampling and
down-sampling structures to expand the receptive field, which results in
texture information loss during the sampling process and some of them design
the multiple stages that lead to difficulties with convergence. Therefore, our
model uses dilated convolution to enable the obtainment of the large receptive
field with high spatial resolution. Through making full use of the different
receptive fields, our method can achieve better performance. On this basis, we
reduce the number of up-sampling and down-sampling and design a simple network
structure. Besides, we propose a novel module using the wavelet transform,
which effectively helps the network to recover clear high-frequency texture
details. Qualitative and quantitative evaluations of real and synthetic
datasets show that our deblurring method is comparable to existing algorithms
in terms of performance with much lower training requirements. The source code
and pre-trained models are available at https://github.com/FlyEgle/SDWNet.
