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.

翻译：近年来，基于深度学习的图像去模糊方法取得了长足发展。然而，在深度学习框架中充分挖掘图像的细节特征通常需要大量参数，这不可避免地导致网络面临高计算负担。为解决该问题，本文提出一种用于图像去模糊的轻量级多信息融合网络（LMFN）。所提出的LMFN采用编码器-解码器架构设计。在编码阶段，图像特征被压缩至多种小尺度空间进行多尺度信息提取与融合，同时避免大量信息损失。随后，在解码阶段引入蒸馏网络，使网络在保持足够轻量化的同时最大程度受益于残差学习。此外，通过注意力机制实现了蒸馏模块与特征通道间的信息融合策略。通过融合所提方法中的不同信息，我们的网络能够以更少的参数量达到当前最优的图像去模糊效果，并在模型复杂度方面优于现有方法。