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

Image deblurring based on lightweight multi-information fusion network