基于递归门控卷积的遥感图像超分辨率研究

Recursive Gated Convolution Based Super-resolution Network for Remote Sensing Images

由于受到硬件条件的限制,通常难以获得具有高分辨率(HR)的遥感图像。利用单幅图像超分辨率(SISR)技术对低分辨率(LR)遥感图像进行超分辨率重建是获取高分辨率遥感图像的常用方法。近年来,在图像超分辨率领域引入的卷积神经网络(CNN)改进了图像重建性能。然而,现有的基于CNN的超分辨率模型通常使用低阶注意力机制提取深层特征,其表征能力有待提高,且常规卷积的感受野有限,缺乏对远距离依赖关系的学习。为了解决以上问题,提出了一种基于递归门控卷积的遥感图像超分辨率方法RGCSR。该方法引入递归门控卷积g n Conv学习全局依赖和局部细节,通过高阶空间交互来获取高阶特征。首先,使用由高阶交互子模块(HorBlock)和前馈神经网络(FFN)组成的高阶交互——前馈神经网络模块(HFB)提取高阶特征。其次,利用由通道注意力(CA)和g n Conv构建的特征优化模块(FOB)优化各个中间模块的输出特征。最后,在多个数据集上的对比结果表明,RGCSR比现有的基于CNN的超分辨率方法具备更好的重建性能和视觉效果。

Due to hardware manufacturing constraints,it is usually difficult to obtain high-resolution(HR)images in the area of remote sensing.From low resolution remote-sensing image to reconstruct high-resolution(HR)image via single-image super-re-solution(SISR)technique is a common method.Recently,the convolutional neural network(CNN)was introduced to the field of super-resolution image reconstruction,and it effectively improved the image reconstruction performance.However,the classic CNN-based approaches typically use low-order attention to extract deep features,which limites its reconstructing ability.More-over,the receptive field is limited,which lacks the ability to learn long-range dependency.To solve the above problems,a recursive gated convolution-based super-resolution method for remote sensing images(RGCSR)is proposed.The RGCSR introduces recursive gated convolution(g n Conv)to learn global dependencies and local details,and high-order features are acquired by high-order spatial interactions.Firstly,a high-order interaction—feedforward neural network(HFB)consisting of a high-order interaction sub-module(HorBlock)and a feedforward neural network(FFN)is applied to extract high-order features.Then,a feature optimization module(FOB)contains channel attention(CA)and g n Conv is used to optimize the output features of each intermediate module.Finally,the comparison results on multiple datasets show that RGCSR has better reconstruction and visualization performances than existing CNN-based solutions.