摘要
光学成像分辨率受衍射极限、探测器尺寸等诸多因素限制。为了获得细节更丰富、纹理更清晰的超分辨率图像,本文提出了一种多尺度特征注意力融合残差网络。首先,使用一层卷积提取图像的浅层特征,之后,通过级联的多尺度特征提取单元提取多尺度特征,多尺度特征提取单元中引入通道注意力模块自适应地校正特征通道的权重,以提高对高频信息的关注度。将网络中的浅层特征和每个多尺度特征提取单元的输出作为全局特征融合重建的层次特征。最后,利用残差分支引入浅层特征和多级图像特征,重建出高分辨率图像。算法使用Charbonnier损失函数使训练更加稳定,收敛速度更快。在国际基准数据集上的对比实验表明:该模型的客观指标优于大多数最先进的方法。尤其在Set5数据集上,4倍重建结果的PSNR指标提升了0.39 dB,SSIM指标提升至0.8992,且算法主观视觉效果更好。
The resolution of optical imaging is limited by the diffraction limit,system detector size and many other factors.To obtain images with richer details and clearer textures,a multi-scale feature attention fusion residual network was proposed.Firstly,shallow features of the image were extracted using a layer of convolution and then the multi-scale features were extracted by a cascade of multi-scale feature extraction units.The local channel attention module is introduced in the multi-scale feature extraction unit to adaptively correct the weights of feature channels and improve the attention to high frequency information.The shallow features and the output of each multi-scale feature extraction unit were used as hierarchical features for global feature fusion reconstruction.Finally,the hight-resolution image was reconstructed by introducing shallow features and multi-level image features using the residual branch.Charbonnier loss was adopted to make the training more stable and converge faster.Comparative experiments on the international benchmark datasets show that the model outperforms most state-of-the-art methods on objective metrics.Especially on the Set5 data set,the PSNR index of the 4×reconstruction result is increased by 0.39 dB,and the SSIM index is increased to 0.8992,and the subjective visual effect of the algorithm is better.
作者
陈纯毅
吴欣怡
胡小娟
于海洋
CHEN Chun-yi;WU Xin-yi;HU Xiao-juan;YU Hai-yang(School of Computer Science and Technology,Changchun University of Science and Technology,Changchun 130022,China)
出处
《中国光学（中英文）》
EI
CAS
CSCD
北大核心
2023年第5期1034-1044,共11页
Chinese Optics
基金
国家自然科学基金项目(No.U19A2063)
吉林省科技发展计划项目(No.20230201080GX)。
关键词
卷积神经网络
超分辨率重建
多尺度特征提取
残差学习
通道注意力机制
convolutional neural network
super-resolution reconstruction
multi-scale feature extraction
re-sidual learning
channel attention mechanism
