  Although prior state-of-the-art (SOTA) deblurring networks achieve high
metric scores on synthetic datasets, there are two challenges which prevent
them from perceptual image deblurring. First, a deblurring model overtrained on
synthetic datasets may collapse in a broad range of unseen real-world
scenarios. Second, the conventional metrics PSNR and SSIM may not correctly
reflect the perceptual quality observed by human eyes. To this end, we propose
DeblurDiNAT, a generalizable and efficient encoder-decoder Transformer which
restores clean images visually close to the ground truth. We adopt an
alternating dilation factor structure to capture local and global blur
patterns. We propose a local cross-channel learner to assist self-attention
layers to learn short-range cross-channel relationships. In addition, we
present a linear feed-forward network and a non-linear dual-stage feature
fusion module for faster feature propagation across the network. Compared to
nearest competitors, our model demonstrates the strongest generalization
ability and achieves the best perceptual quality on mainstream image deblurring
datasets with 3%-68% fewer parameters.
