多阶段特征蒸馏加权的轻量级图像超分辨率网络

A multi-stage feature distillation-weighted lightweight image super-resolution network

针对在轻量化网络中提取底层特征感受野不足以及缺乏对局部关键特征强化的问题,提出一种多阶段特征蒸馏加权的轻量级图像超分辨率网络LMSWN。首先,通过类金字塔模块扩大对浅层特征提取时的感受野,融合不同尺度的特征信息,丰富网络的信息流;其次,设计多阶段残差蒸馏加权模块用于增强方形卷积提取局部关键特征的能力,以恢复更多细节信息提高重建性能,同时将通道分离与1×1卷积结合共同实现对特征的逐级蒸馏,减少网络参数量;最后,引入2个自适应参数对多阶段残差蒸馏加权模块的2条支路特征进行联合学习,提升对不同层次特征信息的关注度,进一步增强网络的表征能力。实验结果表明,在Set 5、Set 14、BSD 100、Urban 100和Manga 109这5个基准测试集上的实验充分验证了所提网络的有效性,其性能超过了当前主流轻量级网络。

To address the issues of insufficient receptive fields for extracting low-level features and the lack of reinforcement for local key features in lightweight networks,this paper proposed a multi-stage feature distillation-weighted lightweight image super-resolution network LMSWN.Firstly,a pyramid-like module is employed to expand the receptive field during shallow feature extraction,integrate feature information of different scales,and enrich the information flow of the network.Secondly,a multi-stage residual distillation-weighted module is designed to enhance the ability of square convolution to extract local key features,recover more detailed information,and improve reconstruction performance.At the same time,the combination of channel separation and 1×1 convolution realizes gradual distillation of features,reducing the number of network parameters.Finally,two adaptive parameters are introduced to jointly learn the features of the two branches of the multi-stage residual distillation-weighted module,enhancing the attention to different levels of feature information and further enhancing the representation ability of the network.Experimental results show that the proposed network is fully validated on five benchmark datasets:Set 5,Set 14,BSDS 100,Urban 100,and Manga 109,and its performance exceeds the current mainstream lightweight network.