  Recently, deep learning based image deblurring has been well developed.
However, exploiting the detailed image features in a deep learning framework
always requires a mass of parameters, which inevitably makes the network suffer
from high computational burden. To solve this problem, we propose a lightweight
multiinformation fusion network (LMFN) for image deblurring. The proposed LMFN
is designed as an encoder-decoder architecture. In the encoding stage, the
image feature is reduced to various smallscale spaces for multi-scale
information extraction and fusion without a large amount of information loss.
Then, a distillation network is used in the decoding stage, which allows the
network benefit the most from residual learning while remaining sufficiently
lightweight. Meanwhile, an information fusion strategy between distillation
modules and feature channels is also carried out by attention mechanism.
Through fusing different information in the proposed approach, our network can
achieve state-of-the-art image deblurring result with smaller number of
parameters and outperforms existing methods in model complexity.
