Single-Image Super-Resolution Reconstruction Based on Improved Attention in A<sup>2</sup>N

Hualiang Cao; Wei Zhuang

doi:10.3788/LOP241193

Laser & Optoelectronics Progress, Volume. 62, Issue 2, 0237010(2025)

Single-Image Super-Resolution Reconstruction Based on Improved Attention in A²N

Hualiang Cao^* and Wei Zhuang

Jiangsu Key Laboratory of Opto-Electronic Technology, School of Physics and Technology, Nanjing Normal University, Nanjing 210023, Jiangsu , China

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References(32)

[1] Lu Z S, Kan X, Li Y et al. Super-resolution reconstruction of FY-4 images based on matching extraction and cross-scale feature fusion network[J]. Laser & Optoelectronics Progress, 60, 1410013(2023).

[2] Lu Y Q, Chen M H, Qin K B et al. Super-resolution reconstruction of OCT image based on pyramid long-range transformer[J]. Chinese Journal of Lasers, 50, 1507107(2023).

[3] Xie Y B, Xu N T, Zhou S et al. Super-resolution image reconstruction of distributed infrared array camera[J]. Laser & Optoelectronics Progress, 59, 1611004(2022).

[4] Dong C, Loy C C, He K M et al. Learning a deep convolutional network for image super-resolution[M]. Computer vision-ECCV 2014, 8692, 184-199(2014).

[5] Kim J, Lee J K, Lee K M. Accurate image super-resolution using very deep convolutional networks[C], 1646-1654(2016).

[6] Lai W S, Huang J B, Ahuja N et al. Deep Laplacian pyramid networks for fast and accurate super-resolution[C], 5835-5843(2017).

[7] Kim J, Lee J K, Lee K M. Deeply-recursive convolutional network for image super-resolution[C], 1637-1645(2016).

[8] Zhang Y L, Li K P, Li K et al. Image super-resolution using very deep residual channel attention networks[EB/OL]. https://arxiv. org/abs/1807.02758

[9] Dai T, Cai J R, Zhang Y B et al. Second-order attention network for single image super-resolution[C], 11057-11066(2019).

[10] Chen H Y, Gu J J, Zhang Z. Attention in attention network for image super-resolution[EB/OL]. http://arxiv.org/abs/2104.09497v3

[11] Li Y, Zhang Y, Timofte R et al. NTIRE 2023 challenge on efficient super-resolution: methods and results[C], 1921-1959(2023).

[12] Hui Z, Gao X B, Yang Y C et al. Lightweight image super-resolution with information multi-distillation network[C], 2024-20(2019).

[13] Zhao H Y, Kong X T, He J W et al. Efficient image super-resolution using pixel attention[M]. Computer vision-ECCV 2022 workshops, 12537, 56-72(2020).

[14] Ahn N, Kang B, Sohn K A. Fast, accurate, and lightweight super-resolution with cascading residual network[M]. Computer vision-ECCV 2018, 11214, 256-272(2018).

[15] Hui Z, Wang X M, Gao X B. Fast and accurate single image super-resolution via information distillation network[C], 723-731(2018).

[16] Liu J, Tang J, Wu G S. Residual feature distillation network for lightweight image super-resolution[M]. Computer vision-ECCV 2020 workshops, 12537, 41-55(2020).

[17] Kong F Y, Li M X, Liu S W et al. Residual local feature network for efficient super-resolution[C], 765-775(2022).

[18] Gholamalinezhad H, Khosravi H. Pooling methods in deep neural networks, a review[EB/OL]. http://arxiv.org/abs/2009.07485v1

[19] Shang T Z, Dai Q J, Zhu S C et al. Perceptual extreme super resolution network with receptive field block[C], 1778-1787(2020).

[20] Liu S T, Huang D, Wang Y H. Receptive field block net for accurate and fast object detection[M]. Computer vision-ECCV 2018, 11215, 404-419(2018).

[21] Wang X T, Yu K, Wu S X et al. ESRGAN: enhanced super-resolution generative adversarial networks[M]. Computer vision-ECCV 2018 workshops, 11133, 63-79(2019).

[22] Mehta S, Rastegari M, Caspi A et al. ESPNet: efficient spatial pyramid of dilated convolutions for semantic segmentation[M]. Computer vision-ECCV 2018, 11214, 561-580(2018).

[23] 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[C], 1874-1883(2016).

[24] Han W, Chang S Y, Liu D et al. Image super-resolution via dual-state recurrent networks[C], 1654-1663(2018).

[25] Deng W J, Yuan H J, Deng L H et al. Reparameterized residual feature network for lightweight image super-resolution[C], 1712-1721(2023).

[26] Bevilacqua M, Roumy A, Guillemot C et al. Low-complexity single-image super-resolution based on nonnegative neighbor embedding[C]. 135, 3-7(2012).

[27] Zeyde R, Elad M, Protter M. On single image scale-up using sparse-representations[M]. Curves and surfaces, 6920, 711-730(2012).

[28] Martin D, Fowlkes C, Tal D et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics[C], 416-423(2001).

[29] Huang J B, Singh A, Ahuja N. Single image super-resolution from transformed self-exemplars[C], 5197-5206(2015).

[30] Matsui Y, Ito K, Aramaki Y et al. Sketch-based manga retrieval using manga109 dataset[J]. Multimedia Tools and Applications, 76, 21811-21838(2017).

[31] Zhao H Y, Kong X T, He J W et al. Efficient image super-resolution using pixel attention[M]. Computer vision-ECCV 2020 workshops, 12537, 56-72(2020).

[32] Wang C F, Li Z, Shi J. Lightweight image super-resolution with adaptive weighted learning network[EB/OL]. http://arxiv.org/abs/1904.02358v1

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Hualiang Cao, Wei Zhuang. Single-Image Super-Resolution Reconstruction Based on Improved Attention in A²N[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0237010

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Paper Information

Category: Digital Image Processing

Received: Apr. 29, 2024

Accepted: Jun. 3, 2024

Published Online: Jan. 6, 2025

The Author Email:

DOI:10.3788/LOP241193

CSTR:32186.14.LOP241193

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology