基于递归门控卷积的遥感图像超分辨率研究

doi:10.11896/jsjkx.230800017

Abstract

Abstract: 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(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 optimization module(FOB) contains channel attention(CA) and gⁿConv 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.

Key words: Recursive gated convolution, High-order spatial interaction, Channel attention, Remote sensing images, Super-resolution

CLC Number:

TP751.1

LIU Changxin, WU Ning, HU Lirui, GAO Ba, GAO Xueshan. Recursive Gated Convolution Based Super-resolution Network for Remote Sensing Images[J].Computer Science, 2024, 51(2): 205-216.

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

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