Remote Sensing Image Super-Resolution With Residual Split Attention Mechanism

Chen, Xitong; Wu, Yuntao; Lü, Tao; Kong, Quan; Wang, Jiaming; Yu, Wei

doi:10.1109/jstars.2023.3287894

Cited by 5 publications

(1 citation statement)

References 58 publications

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“…Zhao et al [45] proposed the second-order attention generator adversarial attention network (SA-GAN), which leverages a second-order channel attention mechanism in the generator to fully utilize the prior information in LR images. Chen et al [46] presented the Residual Splitattention Network (RSAN), which utilizes the multipath Residual Split-attention (RSA) mechanism to fuse different channel dimensions to promote feature extraction and ensure that the network focuses more on regions with rich details. Wang et al [47] proposed the Multiscale Enhancement Network (MEN), incorporating a Multiscale Enhancement Module (MEM), which utilizes a parallel combination of convolutional layers comprising kernels of varying sizes to refine the extraction of multiscale features, thereby enhancing the network's reconstruction capabilities.…”

Section: Deep Learning-based Sisr For Remote Sensing Imagesmentioning

confidence: 99%

SRBPSwin: Single-Image Super-Resolution for Remote Sensing Images Using a Global Residual Multi-Attention Hybrid Back-Projection Network Based on the Swin Transformer

Qin,

Wang,

Cao

et al. 2024

Remote Sensing

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Remote sensing images usually contain abundant targets and complex information distributions. Consequently, networks are required to model both global and local information in the super-resolution (SR) reconstruction of remote sensing images. The existing SR reconstruction algorithms generally focus on only local or global features, neglecting effective feedback for reconstruction errors. Therefore, a Global Residual Multi-attention Fusion Back-projection Network (SRBPSwin) is introduced by combining the back-projection mechanism with the Swin Transformer. We incorporate a concatenated Channel and Spatial Attention Block (CSAB) into the Swin Transformer Block (STB) to design a Multi-attention Hybrid Swin Transformer Block (MAHSTB). SRBPSwin develops dense back-projection units to provide bidirectional feedback for reconstruction errors, enhancing the network’s feature extraction capabilities and improving reconstruction performance. SRBPSwin consists of the following four main stages: shallow feature extraction, shallow feature refinement, dense back projection, and image reconstruction. Firstly, for the input low-resolution (LR) image, shallow features are extracted and refined through the shallow feature extraction and shallow feature refinement stages. Secondly, multiple up-projection and down-projection units are designed to alternately process features between high-resolution (HR) and LR spaces, obtaining more accurate and detailed feature representations. Finally, global residual connections are utilized to transfer shallow features during the image reconstruction stage. We propose a perceptual loss function based on the Swin Transformer to enhance the detail of the reconstructed image. Extensive experiments demonstrate the significant reconstruction advantages of SRBPSwin in quantitative evaluation and visual quality.

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Section: Deep Learning-based Sisr For Remote Sensing Imagesmentioning

confidence: 99%