| Citation: |
CHEN Qingjiang, YIN Lexuan, SHAO Luoyi. Image super-resolution reconstruction based on multi-scale two-stage network[J]. Journal of Applied Optics, 2023, 44(6): 1343-1354. DOI: 10.5768/JAO202344.0602004
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Aiming at the problems of the insufficient feature information extraction and the blurring of the reconstructed image details in current image super-resolution reconstruction algorithm, a multi-scale two-stage network was proposed to realize image super-resolution reconstruction. First of all, considering the phenomenon of insufficient feature information extraction in single-scale convolution layer, a network model was designed based on the general framework of multi-scale convolution layer.Secondly, considering the effect of the reconstructed image, the whole network was divided into two stages: the first stage was to extract and reconstruct the feature information according to the input low-resolution image, and the second stage was to further refine the features of the reconstructed image, so as to improve the visual effect of the reconstructed image. Jump connection and attention module were also introduced in the overall network to enhance the effective transmission of feature information. Finally, the data sets Set5, Set14, Urban100, BSDS100 and Manga109 were used as the test sets of the experiment, and the peak signal-to-noise ratio and the structural similarity were used as the evaluation indicators of image quality. The experiment shows that the values of both are improved and the visual effect of reconstructed image is good. Therefore, the algorithm has achieved good results in both objective evaluation and subjective vision.
| [1] |
史振威, 雷森. 图像超分辨重建算法综述[J]. 数据采集与处理,2020,35(1):1-20.
SHI Zhenwei, LEI Sen. Overview of image super resolution reconstruction algorithms[J]. Data Acquisition and Processing,2020,35(1):1-20.
|
| [2] |
MENG B Y, HONG J W, MENG Y L, et al. Overview of research on image super-resolution reconstruction[C]//2021 IEEE International Conference on Information Communication and Software Engineering (ICICSE). New York: IEEE, 2021: 131-135.
|
| [3] |
DONG C, LOY C C, HE K, et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,38(2):295-307.
|
| [4] |
DONG C, LOY C C, TANG X. Accelerating the super-resolution convolutional neural network[C]//European Conference on Computer Vision. Switzerland: Springer, Cham, 2016: 391-407.
|
| [5] |
SHI W, CABALLERO J, HUSZÁR F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2016: 1874-1883.
|
| [6] |
WANG W, HU Y, LUO Y, et al. Brief survey of single image super-resolution reconstruction based on deep learning approaches[J]. Sensing and Imaging,2020,21(1):1-20. doi: 10.1007/s11220-019-0262-y
|
| [7] |
HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2016: 770-778.
|
| [8] |
KIM J, LEE J K, LEE K M. Accurate image super-resolution using very deep convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2016: 1646-1654.
|
| [9] |
KIM J, LEE J K, LEE K M. Deeply-recursive convolutional network for image super-resolution[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2016: 1637-1645.
|
| [10] |
SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2015-04-10)[2023-01-15]. https://browse.arxiv.org/pdf/1409.1556.pdf.
|
| [11] |
周登文, 赵丽娟, 段然, 等. 基于递归残差网络的图像超分辨率重建[J]. 自动化学报,2019,45(6):1157-1165.
ZHOU Dengwen, ZHAO Lijuan, DUAN Ran, et al. Image super-resolution reconstruction based on recursive residual networks[J]. Journal of Automation,2019,45(6):1157-1165.
|
| [12] |
郭继昌, 吴洁, 郭春乐, 等. 基于残差连接卷积神经网络的图像超分辨率重构[J]. 吉林大学学报(工学版),2019,49(5):1726-173.
GUO Jichang, WU Jie, GUO Chunle, et al. Image super-resolution reconstruction based on residual connection convolutional neural network[J]. Journal of Jilin University (Engineering Edition),2019,49(5):1726-173.
|
| [13] |
LIM B, SON S, KIM H, et al. Enhanced deep residual networks for single image super-resolution[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. New York: IEEE, 2017: 136-144.
|
| [14] |
张敏, 黄刚, 陈啟超. 基于残差学习的图像超分辨率重构方法[J]. 计算机技术与发展,2021,31(8):51-56. doi: 10.3969/j.issn.1673-629X.2021.08.009
ZHANG Min, HUANG Gang, CHEN Qichao. Image super-resolution reconstruction method based on residual learning[J]. Computer Technology and Development,2021,31(8):51-56. doi: 10.3969/j.issn.1673-629X.2021.08.009
|
| [15] |
张健, 何京璇, 王容. 基于CNN和Resblock的图像超分辨率重建算法[J]. 信息技术与网络安全,2019,38(7):54-59.
ZHANG Jian, HE Jingxuan, WANG Rong. Image super-resolution reconstruction algorithm based on CNN and Resblock[J]. Information Technology and Network Security,2019,38(7):54-59.
|
| [16] |
ZHANG Y, TIAN Y, KONG Y, et al. Residual dense network for image super-resolution[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2018: 2472-2481.
|
| [17] |
ZHANG Y, LI K, LI K, et al. Image super-resolution using very deep residual channel attention networks[C]//Proceedings of the European Conference on Computer Vision (ECCV). [S. l. ]: Arxiv, 2018: 286-301.
|
| [18] |
魏静波. 残差密集注意力网络的超分辨率重建[J]. 电子技术与软件工程,2021(14):127-128.
WEI Jingbo. Super-resolution reconstruction of residual dense attention networks[J]. Electronic Technology and Software Engineering,2021(14):127-128.
|
| [19] |
DAI T, CAI J, ZHANG Y, et al. Second-order attention network for single image super-resolution[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2019: 11065-11074.
|
| [20] |
NIU B, WEN W, REN W, et al. Single image super-resolution via a holistic attention network[C]//European Conference on Computer Vision. Switzerland: Springer, Cham, 2020: 191-207.
|
| [21] |
李滔, 董秀成, 林宏伟. 基于深监督跨尺度注意力网络的深度图像超分辨率重建[J]. 电子学报,2023,51(1):128-138.
LI Tao, DONG Xiucheng, LIN Hongwei. Deep image super-resolution reconstruction based on deep supervised cross scale attention network[J]. Journal of Electronics,2023,51(1):128-138.
|
| [22] |
FEI Y, LIAN F H, YAN Y. An improved PSNR algorithm for objective video quality evaluation[C]//2007 Chinese Control Conference. New York: IEEE, 2007: 376-380.
|
| [23] |
WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing,2004,13(4):600-612. doi: 10.1109/TIP.2003.819861
|
| [24] |
GREESHMA M S, BINDU V R. Super-resolution using deep networks for chest X-ray images[C]//6th International Conference on Image Information Processing, ICIIP 2021. [S. l. ]: [s. n. ], 2021: 198-201.
|
| [25] |
WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV). [S. l. ]: [s. n. ], 2018: 3-19.
|
| [26] |
KINGMA D P, BA J. Adam: a method for stochastic optimization[EB/OL]. (2017-01-30)[2013-01-15]. https://browse.arxiv.org/pdf/1412.6980.pdf.
|
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