Recursive Gated Convolution Based Super-resolution Network for Remote Sensing Images
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 recur-sive gated convolution(gnConv)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 optimi-zation module(FOB)contains channel attention(CA)and gn 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 per-formances than existing CNN-based solutions.
万方数据
维普
