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
郑剑 1刘子龙 2于祥春2
作者信息
- 1. 江西理工大学信息工程学院,江西 赣州 341000;江西理工大学宜春锂电新能源产业研究院,江西 宜春 336023
- 2. 江西理工大学信息工程学院,江西 赣州 341000
- 折叠
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.
Abstract
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 parameter-free 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Key words
remote sensing image/deep learning/image super-resolution/multiscale feature/parameter-free attention mechanism引用本文复制引用
出版年
2024
NETL
NSTL
万方数据