• Laser & Optoelectronics Progress
  • Vol. 60, Issue 10, 1010017 (2023)
Yanfei Peng, Manting Zhang*, Pingjia Zhang, Jian Li, and Lirui Gu
Author Affiliations
  • School of Electronic and Information Engineering, Liaoning Technical University, Huludao 125105, Liaoning , China
  • show less
    DOI: 10.3788/LOP220752 Cite this Article Set citation alerts
    Yanfei Peng, Manting Zhang, Pingjia Zhang, Jian Li, Lirui Gu. Single-Image Super-Resolution Reconstruction Aggregating Residual Attention Network[J]. Laser & Optoelectronics Progress, 2023, 60(10): 1010017 Copy Citation Text show less
    References

    [1] Yang X M, Wu W, Liu K et al. Long-distance object recognition with image super resolution: a comparative study[J]. IEEE Access, 6, 13429-13438(2018).

    [2] Huang S, Hu Y, Gu M J et al. Super-resolution infrared remote-sensing target-detection algorithm based on deep learning[J]. Laser & Optoelectronics Progress, 58, 1610015(2021).

    [3] Wang Y K, Teng Q Z, He X H et al. CT-image of rock samples super resolution using 3D convolutional neural network[J]. Computers & Geosciences, 133, 104314(2019).

    [4] Heckel R, Morgenshtern V I, Soltanolkotabi M. Super-resolution radar[J]. Information and Inference: A Journal of the IMA, 5, 22-75(2016).

    [5] Yang F F, Li H, Peng J et al. Research on microscopic imaging of high resolution light field based on graph regularization[J]. Acta Optica Sinica, 41, 0918001(2021).

    [6] Zhu S Y, Zeng B, Zeng L Y et al. Image interpolation based on non-local geometric similarities and directional gradients[J]. IEEE Transactions on Multimedia, 18, 1707-1719(2016).

    [7] Beck A, Teboulle M. Convergence rate analysis and error bounds for projection algorithms in convex feasibility problems[J]. Optimization Methods and Software, 18, 377-394(2003).

    [8] Li Y, Jin Q Y, Zhao H C et al. Hyperspectral image reconstruction based on improved residual dense network[J]. Acta Optica Sinica, 41, 0730001(2021).

    [9] Dong C, Loy C C, He K M et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 295-307(2016).

    [10] 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).

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

    [12] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

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

    [14] Zhang Y L, Tian Y P, Kong Y et al. Residual dense network for image super-resolution[C], 2472-2481(2018).

    [15] Zhang Y L, Li K P, Li K et al. Image super-resolution using very deep residual channel attention networks[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 294-310(2018).

    [16] Ayazoglu M. Extremely lightweight quantization robust real-time single-image super resolution for mobile devices[C], 2472-2479(2021).

    [17] Xie S N, Girshick R, Dollár P et al. Aggregated residual transformations for deep neural networks[C], 5987-5995(2017).

    [18] Goodfellow I J, Pouget-Abadie J, Mirza M et al. Generative adversarial networks[EB/OL]. https://arxiv.org/abs/1406.2661

    [19] Ledig C, Theis L, Huszár F et al. Photo-realistic single image super-resolution using a generative adversarial network[C], 105-114(2017).

    [20] Wang X T, Yu K, Wu S X et al. ESRGAN: enhanced super-resolution generative adversarial networks[M]. Leal-Taixé L, Roth S. Computer vision-ECCV 2018 workshops. Lecture notes in computer science, 11133, 63-79(2019).

    [21] Xin Y X, Zhu F T, Shi P F et al. Super-resolution reconstruction algorithm of images based on improved enhanced super-resolution generative adversarial network[J]. Laser & Optoelectronics Progress, 59, 0420002(2022).

    [22] Yang L, Zhang R Y, Li L et al. Simam: a simple, parameter-free attention module for convolutional neural networks[C], 11863-11874(2021).

    [23] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2018).

    [24] Lei P C, Liu C, Tang J G et al. Hierarchical feature fusion attention network for image super-resolution reconstruction[J]. Journal of Image and Graphics, 25, 1773-1786(2020).

    [25] Webb B S, Dhruv N T, Solomon S G et al. Early and late mechanisms of surround suppression in striate cortex of macaque[J]. The Journal of Neuroscience, 25, 11666-11675(2005).

    [26] Miyato T, Kataoka T, Koyama M et al. Spectral normalization for generative adversarial networks[EB/OL]. https://arxiv.org/abs/1802.05957

    [27] Timofte R, Agustsson E, van Gool L et al. Ntire 2017 challenge on single image super resolution: methods and results[C], 114-125(2017).

    [28] Bevilacqua M, Roumy A, Guillemot C et al. Low-complexity single-image super-resolution based on nonnegative neighbor embedding[C], 1-10(2012).

    [29] Zeyde R, Elad M, Protter M. On single image scale-up using sparse-representations[M]. Boissonnat J D, Chenin P, Cohen A, et al. Curves and surfaces. Lecture notes in computer science, 6920, 711-730(2012).

    [30] 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).

    Yanfei Peng, Manting Zhang, Pingjia Zhang, Jian Li, Lirui Gu. Single-Image Super-Resolution Reconstruction Aggregating Residual Attention Network[J]. Laser & Optoelectronics Progress, 2023, 60(10): 1010017
    Download Citation