Abstract
Aerial images are often applied into disaster area surveillance. High-resolution (HR) aerial images are preferred to monitor the disaster area since they can provide abundant information. However, limited by hardware device and imaging environment, the resolution of captured aerial images may not meet the needs of practical application. Image super-resolution (SR) is an effective way to improve the resolution of captured images in a post-processing manner. Recently, convolutional neural networks (CNNs) have demonstrated great success in image SR. However, these CNN models cannot be easily applied to real-world scenarios due to requiring huge storage and computational resources. To reduce resource consumption, we need to decrease network parameters. Recursive network can effectively reduce network parameters, which motivates us to explore a more effective image SR method. In this paper, we proposed a deep recursive dense network (DRDN) to reconstruct HR aerial images. In the DRDN, the proposed recursive dense block (RDB) can fully extract abundant local features and adaptively fuse different hierarchical features of LR image for HR image reconstruction. In addition, the recursive manner of RDB in DRDN can effectively reduce the parameter of network. The experimental results on aerial images demonstrate the superiority of our proposed method.
Similar content being viewed by others
References
Yi Y, Xi C, Di Z, Yunzhi W u, Zhao Y, Chen Y, Fan G, Zhang Y (2018) Deep recursive super resolution network with laplacian pyramid for better agricultural pest surveillance and detection. Comput Electron Agric 150:26–32
Shamsolmoali P, Zareapoor M, Jain DK, Jain VK, Yang J (2019) Deep convolution network for surveillance records super-resolution. Multimed Tools Appl 78(17):23815–23829
Lei J, Zhang S, Li L, Xiao J, He W (2018) Super-resolution enhancement of uav images based on fractional calculus and pocs. Geo-Spat Inf Sci 21(1):56–66
Batz M, Eichenseer A, Seiler J, Jonscher M, Kaup A (2015) Hybrid super-resolution combining example-based single-image and interpolation-based multi-image reconstruction approaches. In: Proceedings of the ICIP, pp 58–62
Zhang L, Xiaolin W u (2006) An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans Image Process 15(8):2226–2238
Zhang K, Gao X, Tao D, Li X (2012) Single image super-resolution with non-local means and steering kernel regression. IEEE Trans. Image Process. 21(11):4544–4556
Yang J, Wright J, Huang T, Yi M a (2010) Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11):2861–2873
Timofte R, Smet VD, Gool LV (2013) Anchored neighborhood regression for fast example-based super-resolution. In: Proceedings of the ICCV, pp 1920–1927
Timofte R, Smet VD, Van Gool L (2014) A+: adjusted anchored neighborhood regression for fast super-resolution. In: Proceedings of the ACCV. Springer, pp 111–126
Peleg T, Elad M (2014) A statistical prediction model based on sparse representations for single image super-resolution. IEEE Trans Image Process 23(6):2569–2582
Schulter S, Leistner C, Bischof H (2015) Fast and accurate image upscaling with super-resolution forests. In: Proceedings of the CVPR, pp 3791–3799
Huang Jia-Bin, Singh A, Ahuja N (2015) Single image super-resolution from transformed self-exemplars. In: Proceedings of the CVPR, pp 5197–5206
Yang J, Wang Z, Lin Z, Cohen S, Huang T (2012) Coupled dictionary training for image super-resolution. IEEE Trans Image Process 21(8):3467–3478
Dong C, Loy CC, He K, Tang X (2014) Learning a deep convolutional network for image super-resolution. In: Proceedings of the ECCV. Springer, pp 184–199
Dong C, Loy CC, He K, Tang X (2015) Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2):295–307
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv: Computer Vision and Pattern Recognition
Kim J, Lee JK, Lee KM (2016) Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the CVPR, pp 1646–1654
Shi W, Caballero J, Huszár F, Totz J, Aitken AP, Bishop R, Rueckert D, Wang Z (2016) Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the CVPR, pp 1874–1883
Dong C, Loy CC, Tang X (2016) Accelerating the super-resolution convolutional neural network. In: Proceedings of the ECCV. Springer, pp 391–407
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the CVPR, pp 770– 778
Lim B, Son S, Kim H, Nah S, Lee KM (2017) Enhanced deep residual networks for single image super-resolution. In: Proceedings of the CVPRW, pp 136–144
Timofte R, Agustsson E, Gool LV, Yang M-H, Zhang L (2017) Ntire 2017 challenge on single image super-resolution: methods and results. In: Proceedings of the CVPRW, pp 114– 125
Zhang Y, Tian Y, Yu K, Zhong B, Fu Y (2018) Residual dense network for image super-resolution. In: Proceedings of the CVPR, pp 2472–2481
Zhang Y, Li K, Li K, Wang L, Zhong B, Fu Y (2018) Image super-resolution using very deep residual channel attention networks. In: Proceedings of the ECCV, pp 286–301
Huang G, Liu Z, Maaten LVD, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the CVPR, pp 4700–4708
Tong T, Li G, Liu X, Gao Q (2017) Image super-resolution using dense skip connections. In: Proceedings of the ICCV, pp 4799–4807
Tai Y, Yang J, Liu X, Xu C (2017) Memnet: a persistent memory network for image restoration. In: Proceedings of the ICCV, pp 4539–4547
Lai Wei-Sheng, Huang Jia-Bin, Ahuja N, Yang Ming-Hsuan (2017) Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proceedings of the CVPR, pp 624–632
Kim J, Lee JK, Lee KM (2016) Deeply-recursive convolutional network for image super-resolution. In: Proceedings of the CVPR, pp 1637–1645
Tai Y, Yang J, Liu X (2017) Image super-resolution via deep recursive residual network. In: Proceedings of the CVPR, pp 3147–3155
Han W, Chang S, Liu D, Yu M, Witbrock M, Huang T (2018) Image super-resolution via dual-state recurrent networks. In: Proceedings of the CVPR, pp 1654–1663
Li Z, Li Q, Wu W, Yang J, Li Z, Yang X (2020) Deep recursive up-down sampling networks for single image super-resolution. Neurocomputing 398:377–388
He K, Zhang X, Ren S, Sun J (2016) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the ICCV, pp 1026–1034
Zhang K, Zuo W, Zhang L (2018) Learning a single convolutional super-resolution network for multiple degradations. In: Proceedings of the CVPR, pp 3262–3271
He Z, Patel VM (2018) Density-aware single image de-raining using a multi-stream dense network. In: Proceedings of the CVPR, pp 695–704
He Z, Patel VM (2018) Densely connected pyramid dehazing network. In: Proceedings of the CVPR, pp 3194–3203
Huang C, Li Y, Chen CL, Tang X (2019) Deep imbalanced learning for face recognition and attribute prediction. IEEE Trans Pattern Anal Mach Intell 1–1
Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: ICML
Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the ICML, pp 448–456
Zhao H, Gallo O, Frosio I, Kautz J (2016) Loss functions for image restoration with neural networks. IEEE Trans Comput Imaging 3(1):47–57
Haris M, Shakhnarovich G, Ukita N (2018) Deep back-projection networks for super-resolution. In: Proceedings of the CVPR, pp 1664–1673
Agustsson E, Timofte R (2017) Ntire 2017 challenge on single image super-resolution: dataset and study. In: Proceedings of the CVPRW, pp 126–135
Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv:1412.6980
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612
Funding
This work was partially supported by the National Natural Science Foundation of China (No.617013 27, No.61711540303, and No.61901285), The funding from Sichuan University under grant 2020SCUNG205, China Postdoctoral Science Foundation Funded Project under Grant 2018M64091 6, Key Research and Development Project of Science and Technology Commission Foundation of Sichuan Province (201 8FZ0036).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Feiqiang Liu and Qiang Yu have equal contribution.
Rights and permissions
About this article
Cite this article
Liu, F., Yu, Q., Chen, L. et al. Aerial image super-resolution based on deep recursive dense network for disaster area surveillance. Pers Ubiquit Comput 26, 1205–1214 (2022). https://doi.org/10.1007/s00779-020-01516-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00779-020-01516-x