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Deblur image using transformers that modulate self-attention outputs dynamically with DeblurDiNAT

Deblur image using transformers that modulate self-attention outputs dynamically with DeblurDiNAT


DeblurDiNAT: A Lightweight and Effective Transformer for Image Deblurring



Blurry images may contain local and global non-uniform artifacts, which complicate the deblurring process and make it more challenging to achieve satisfactory results.


... propose DeblurDiNAT, a compact encoder-decoder Transformer which efficiently restores clean images from real-world blurry ones.


... adopt an alternating dilation factor structure with the aim of global-local feature learning.


... propose a channel modulation self-attention (CMSA) block, where a cross-channel learner (CCL) is utilized to capture channel relationships.


... present a divide and multiply feed-forward network (DMFN) allowing fast feature propagation ... design a lightweight gated feature fusion (LGFF) module, which performs controlled feature merging.


... DeblurDiNAT, provides ... achieves state-of-the-art (SOTA) ... on ... image deblurring datasets ... space-efficient and time-saving method ... stronger generalization ... 3%-68% fewer parameters ... deblurred ... closer to the ground truth.



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