基于多尺度无参数注意力机制增强网络的遥感图像超分辨率重建  

Remote Sensing Image Super-Resolution Reconstruction Based on Multiscale Parameter-Free Attention Mechanism Enhanced Network

作　　者：郑剑[1,2] 刘子龙 于祥春 Zheng Jian;Liu Zilong;Yu Xiangchun(School of Information Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,Jiangxi,China;Yichun Lithium New Energy Industry Research Institute,Jiangxi University of Science and Technology,Yichun 336023,Jiangxi,China)

机构地区：[1]江西理工大学信息工程学院,江西赣州341000 [2]江西理工大学宜春锂电新能源产业研究院,江西宜春336023

出　　处：《激光与光电子学进展》2024年第24期269-275,共7页Laser & Optoelectronics Progress

基　　金：江西省自然科学基金(2022BAB212013);宜春市科技专项资金(2023YBKJGG02)。

摘　　要：为获取具有更多高频信息和纹理细节信息的遥感图像,并解决遥感图像超分辨率网络结构复杂、参数过多和模型规模大的问题,提出一种多尺度无参数注意力机制的增强网络。该网络利用卷积层提取低分辨率遥感图像的浅层特征,将浅层特征输入多尺度无参数注意力增强模块中,该模块利用多个不同大小卷积核的卷积层并行连接组合来细化多尺度特征的提取,在无参数注意力机制下,通过对称激活函数增强具有高贡献的特征信息,抑制冗余信息。经过6个残差连接的多尺度无参数注意力增强模块后,由重建模块生成最终的重构图像。实验结果表明,与现行具有代表性的方法进行对比,所提网络在性能指标和视觉效果方面都具有显著的重建优势,峰值信噪比、结构相似性等指标均优于其他对比方法。To obtain remote sensing images with more high-frequency information and textural detail information and solve the problems of super-resolution networks,such as complex structure,numerous parameters and large model size,this paper proposes a multiscale parameter-free attention mechanism enhanced network.First,the proposed network uses convolutional layers to extract shallow features from low-resolution remote sensing images.The shallow features are then input to the proposed multiscale parameter-free attention enhancement network,which combines parallel connection of multiple convolutional layers with different-sized convolutional kernels to refine the extraction of multiscale features.The proposed network also enhances feature information with a high contribution via the symmetric activation function to inhibit redundant information under the parameter-free attention mechanism.After six residual-connected multiscale parameterfree attention enhancement modules,the reconstruction module generates the final reconstructed image.Experimental results demonstrate that compared with the existing representative methods,the proposed network exhibits significant reconstruction advantages in terms of performance metrics and visual effects.Moreover,the peak signal-to-noise ratio and structural similarity of the proposed network outperformed those of the compared methods.

关 键 词：遥感图像 深度学习 图像超分辨率 多尺度特征 无参数注意力机制 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]