DeblurDiNAT: A Lightweight and Effective Transformer for Image
  Deblurring
Hanzhou Liu Binghan Li Chengkai Liu Mi Lu
Blurry images may contain local and global non-uniform artifacts, which
complicate the deblurring process and make it more challenging to achieve
satisfactory results. Recently, Transformers generate improved deblurring
outcomes than existing CNN architectures. However, the large model size and
long inference time are still two bothersome issues which have not been fully
explored. To this end, we propose DeblurDiNAT, a compact encoder-decoder
Transformer which efficiently restores clean images from real-world blurry
ones. We adopt an alternating dilation factor structure with the aim of
global-local feature learning. Also, we observe that simply using
self-attention layers in networks does not always produce good deblurred
results. To solve this problem, we propose a channel modulation self-attention
(CMSA) block, where a cross-channel learner (CCL) is utilized to capture
channel relationships. In addition, we present a divide and multiply
feed-forward network (DMFN) allowing fast feature propagation. Moreover, we
design a lightweight gated feature fusion (LGFF) module, which performs
controlled feature merging. Comprehensive experimental results show that the
proposed model, named DeblurDiNAT, provides a favorable performance boost
without introducing noticeable computational costs over the baseline, and
achieves state-of-the-art (SOTA) performance on several image deblurring
datasets. Compared to nearest competitors, our space-efficient and time-saving
method demonstrates a stronger generalization ability with 3%-68% fewer
parameters and produces deblurred images that are visually closer to the ground
truth.