Abstract：The image super-resolution reconstruction algorithm generates a poor effect for the remote sensing images due to different sizes of ground objects and high complexity in the images. Aiming at this problem, a dual-parallel lightweight residual attention network is proposed to increase the reconstruction result. Firstly, a multi-scale shallow feature extraction block(MFEB) is put forward to gain the feature information of different receptive field sizes. The problem of the ground objects with different sizes can be solved by MFEB. Secondly, a lightweight residual attention block(LRAB) is designed with asymmetric convolution and attention mechanism. And thus, the model parameters are reduced and more high-frequency information is captured. Then, the parallel network with different convolution kernels is designed to fuse different receptive fields. Besides, lots of skip connections are employed in residual blocks to increase the reusability of information. Finally, experiments show that the proposed model produces superior performance.
[1] DONG C, LOY C C, HE K M, et al. Image Super-Resolution Using Deep Convolutional Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2): 295-307. [2] LIEBEL L, KÖRNER M. Single-Image Super Resolution for Multispectral Remote Sensing Data Using Convolutional Neural Networks. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, XLI-B3: 883-890. [3] HE K M, ZHANG X Y, REN S Q, et al. Deep Residual Learning for Image Recognition // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 770-778. [4] LIM B, SON S, KIM H, et al. Enhanced Deep Residual Networks for Single Image Super-Resolution // Proc of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. Washington, USA: IEEE, 2017: 1132-1140. [5] LEI S, SHI Z W, ZOU Z X. Super-Resolution for Remote Sensing Images via Local-Global Combined Network. IEEE Geoscience and Remote Sensing Letters, 2017, 14(8): 1243-1247. [6] PAN Z X, MA W, GUO J Y, et al. Super-Resolution of Single Remote Sensing Image Based on Residual Dense Back-Projection Networks. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(10): 7918-7933. [7] HU J, SHEN L, ALBANIE S, et al. Squeeze-and-Excitation Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011-2023. [8] WOO S, PARK J, LEE J, et al. CBAM: Convolutional Block Attention Module // Proc of the European Conference on Computer Vision. Berlin, Germany, Springer, 2018: 3-19. [9] WOO S, PARK J, LEE J Y, et al. BAM: Bottleneck Attention Module[C/OL]. [2021-05-06]. http://arxiv.org/abs/1807.06514. [10] ZHANG Y L, LI K P, LI K, et al. Image Super-Resolution Using Very Deep Residual Channel Attention Networks // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2018: 294-310. [11] GU J, XU G L, ZHANG Y. Wider Channel Attention Network for Remote Sensing Image Super-Resolution[C/OL]. [2021-05-06]. https://arxiv.org/pdf/1812.05329.pdf. [12] DONG X Y, SUN X, JIA X P, et al. Remote Sensing Image Super-Resolution Using Novel Dense-Sampling Networks. IEEE Tran-sactions on Geoscience and Remote Sensing, 2021, 59(2): 1618-1633. [13] HAUT J M, FERNANDEZ-BELTRAN R, PAOLETTI M E, et al. Remote Sensing Image Super-Resolution Using Deep Residual Channel Attention. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(11): 9277-9289. [14] DONG R M, ZHANG L X, FU H H. RRSGAN: Reference-Based Super-Resolution for Remote Sensing Image[C/OL]. [2021-05-06]. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9328132. [15] SZEGEDY C, LIU W, JIA Y Q, et al. Going Deeper with Convolutions // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA, IEEE, 2015. DOI: 10.1109/CVPR.2015.7298594. [16] SZEGEDY C, VANHOUCKE V, LOFFE S, et al. Rethinking the Inception Architecture for Computer Vision // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 2818-2826. [17] CHOLLET F. Xception: Deep Learning with Depthwise Separable Convolutions // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2017: 1800-1807. [18] HOWARD A G, ZHU M L, CHEN B, et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications[C/OL]. [2021-05-06]. https://arxiv.org/pdf/1704.04861.pdf. [19] MA N N, ZHANG X Y, ZHENG H T, et al. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design // Proc of the European Conference on Computer Vision. Berlin, Germany, Springer, 2018: 122-138. [20] ZHANG X Y, ZHOU X Y, LIN M X, et al. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices // Proc of the IEEE/CVF Conference on Computer Vision and Pa-ttern Recognition. Washington, USA: IEEE, 2018: 6848-6856. [21] DONG X Y, XI Z H, SUN X, et al. Transferred Multi-perception Attention Networks for Remote Sensing Image Super-Resolution. Remote Sensing, 2019, 11(23). DOI: 10.3390/rs11232857. [22] SHI W Z, CABALLERO J, HUSZÁR F, et al. Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-pixel Convolutional Neural Network // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 1874-1883. [23] HUI Z, WANG X M, GAO X B. Fast and Accurate Single Image Super-Resolution via Information Distillation Network // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2018: 723-731. [24] XIE H P, JIANG H Y, LIU X Y, et al. Super Resolution for Remote Sensing Images via Improved Residual Network // Proc of the 5th International Conference on Mechanical, Control and Computer Engineering. Washington, USA: IEEE, 2020: 2295-2298.
2021, Vol. 34 
