Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (3): 930-937.DOI: 10.11772/j.issn.1001-9081.2021030434
• Multimedia computing and computer simulation • Previous Articles
Leping LIN1,2, Hongmin ZHOU2, Ning OUYANG1,2()
Received:
2021-03-22
Revised:
2021-06-12
Accepted:
2021-06-17
Online:
2022-04-09
Published:
2022-03-10
Contact:
Ning OUYANG
About author:
LIN Leping, born in 1980, Ph. D., associate professor. Her research interests include machine learning, intelligent information processing, image signal processing.Supported by:
通讯作者:
欧阳宁
作者简介:
林乐平(1980—),女,广西桂平人,副教授,博士,主要研究方向:机器学习、智能信息处理、图像信号处理基金资助:
CLC Number:
Leping LIN, Hongmin ZHOU, Ning OUYANG. Compressed sensing image reconstruction method fusing spatial location and structure information[J]. Journal of Computer Applications, 2022, 42(3): 930-937.
林乐平, 周宏敏, 欧阳宁. 融合空间位置与结构信息的压缩感知图像重建方法[J]. 《计算机应用》唯一官方网站, 2022, 42(3): 930-937.
Add to citation manager EndNote|Ris|BibTeX
URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021030434
方法 | SR=0.01 | SR=0.05 | SR=0.10 | SR=0.15 | ||||
---|---|---|---|---|---|---|---|---|
PSNR/dB | SSIM | PSNR/dB | SSIM | PSNR/dB | SSIM | PSNR/dB | SSIM | |
TVAL3 | 13.17 | 0.310 4 | 20.02 | 0.509 7 | 22.86 | 0.639 6 | 24.91 | 0.724 5 |
D-AMP | 5.11 | 0.007 6 | 17.13 | 0.433 9 | 20.98 | 0.571 4 | 23.99 | 0.697 8 |
ReconNet | 17.84 | 0.427 6 | 21.43 | 0.578 5 | 24.15 | 0.698 4 | 25.65 | 0.754 3 |
NL-MRN | 17.78 | 0.430 5 | 21.95 | 0.613 7 | 24.68 | 0.721 4 | 26.61 | 0.788 8 |
SLSI | 19.46 | 0.4693 | 22.75 | 0.6415 | 25.60 | 0.7675 | 27.19 | 0.8135 |
Tab. 1 Comparison of average PSNR and average SSIM among different methods on 6 test images
方法 | SR=0.01 | SR=0.05 | SR=0.10 | SR=0.15 | ||||
---|---|---|---|---|---|---|---|---|
PSNR/dB | SSIM | PSNR/dB | SSIM | PSNR/dB | SSIM | PSNR/dB | SSIM | |
TVAL3 | 13.17 | 0.310 4 | 20.02 | 0.509 7 | 22.86 | 0.639 6 | 24.91 | 0.724 5 |
D-AMP | 5.11 | 0.007 6 | 17.13 | 0.433 9 | 20.98 | 0.571 4 | 23.99 | 0.697 8 |
ReconNet | 17.84 | 0.427 6 | 21.43 | 0.578 5 | 24.15 | 0.698 4 | 25.65 | 0.754 3 |
NL-MRN | 17.78 | 0.430 5 | 21.95 | 0.613 7 | 24.68 | 0.721 4 | 26.61 | 0.788 8 |
SLSI | 19.46 | 0.4693 | 22.75 | 0.6415 | 25.60 | 0.7675 | 27.19 | 0.8135 |
方法 | SR值 | ||
---|---|---|---|
0.01 | 0.04 | 0.10 | |
ReconNet | 0.408 3 | 0.526 6 | 0.641 6 |
DR2-Net | 0.429 1 | 0.580 4 | 0.717 4 |
MSRNet | 0.453 5 | 0.616 7 | 0.759 8 |
SLSI | 0.494 3 | 0.629 7 | 0.773 2 |
Tab. 2 Comparison of average SSIM of different methods on Set11 dataset
方法 | SR值 | ||
---|---|---|---|
0.01 | 0.04 | 0.10 | |
ReconNet | 0.408 3 | 0.526 6 | 0.641 6 |
DR2-Net | 0.429 1 | 0.580 4 | 0.717 4 |
MSRNet | 0.453 5 | 0.616 7 | 0.759 8 |
SLSI | 0.494 3 | 0.629 7 | 0.773 2 |
1 | CANDES E J, WAKIN M B. An introduction to compressive sampling [J]. IEEE Signal Processing Magazine, 2008, 25(2): 21-30. 10.1109/msp.2007.914731 |
2 | 陈伟业, 孙权森. 结合压缩感知与非局部信息的图像超分辨率重建[J]. 计算机应用, 2016, 36(9): 2570-2575. 10.11772/j.issn.1001-9081.2016.09.2570 |
CHEN W Y, SUN Q S. Image super-resolution reconstruction combined with compressed sensing and nonlocal information [J]. Journal of Computer Applications, 2016, 36(9): 2570-2575. 10.11772/j.issn.1001-9081.2016.09.2570 | |
3 | LIN L P, FANG L, JIAO L C. Geometric structure guided collaborative compressed sensing [J]. Signal Processing: Image Communication, 2016, 40:16-25. 10.1016/j.image.2015.10.006 |
4 | SUN Z Y, WANG H H, LIU B L, et al. CS-FCDA: a compressed sensing-based on fault-tolerant data aggregation in sensor networks [J]. Sensors, 2018, 18(11): 3749. 10.3390/s18113749 |
5 | YANG J, LU X, SU W, et al. Multistatic inverse synthetic aperture radar imaging based on parametric block-sparse reconstruction [J]. Journal of Applied Remote Sensing, 2020, 14(2):1. 10.1117/1.jrs.14.026501 |
6 | GAO X W, ZHANG J, CHE W, et al. Block-based compressive sensing coding of natural images by local structural measurement matrix [C]// Proceedings of 2015 the Data Compression Conference. Piscataway: IEEE, 2015:133-142. 10.1109/dcc.2015.47 |
7 | KULKARNI K, LOHIT S, TURAGA P, et al. ReconNet: non-iterative reconstruction of images from compressively sensed measurements [C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2016: 449-458. 10.1109/cvpr.2016.55 |
8 | YAO H, DAI F, ZHANG D, et al. DR2-Net: deep residual reconstruction network for image compressive sensing [J]. Neurocomputing, 2019, 359: 483-493. 10.1016/j.neucom.2019.05.006 |
9 | HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition [C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2016: 770-778. 10.1109/cvpr.2016.90 |
10 | LI W, LIU F, JIAO L, et al. Multi-Scale residual reconstruction neural network with non-local constraint [J]. IEEE Access, 2019, 7: 70910-70918. 10.1109/access.2019.2918593 |
11 | KAI X, ZHANG Z, REN F B. LAPRAN: a scalable laplacian pyramid reconstructive adversarial network for flexible compressive sensing reconstruction [C]// Proceedings of the 2018 European Conference on Computer Vision. Cham: Springer, 2018: 485-500. 10.1007/978-3-030-01249-6_30 |
12 | 练秋生,富利鹏,陈书贞,等.基于多尺度残差网络的压缩感知重建方法[J].自动化学报,2019,45(11):2082-2091. |
LIAN Q S, FU L P, CHEN S Z, et al. A compressed sensing algorithm based on multi-scale residual reconstruction network [J]. Acta Automatica Sinica, 2019, 45(11): 2082-2091. | |
13 | HUANG H, NIE G, ZHENG Y, et al. Image restoration from patch-based compressed sensing measurement [J]. Neurocomputing, 2019, 340: 145-157. 10.1016/j.neucom.2019.02.036 |
14 | 杜秀丽,张薇,陈波.基于波浪式矩阵置换的稀疏度均衡分块压缩感知算法[J].计算机应用,2018,38(12):3541-3546. 10.11772/j.issn.1001-9081.2018051039 |
DU X L, ZHANG W, CHEN B. Ripple matrix permutation-based sparsity balanced block compressed sensing algorithm [J]. Journal of Computer Applications, 2018,38(12):3541-3546. 10.11772/j.issn.1001-9081.2018051039 | |
15 | JIE H, LI S, GANG S, et al. Squeeze-and-excitation networks [C]// Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 7132-7141. 10.1109/cvpr.2018.00745 |
16 | KRIZHEVSKY A, SUTSKEVER I, HINTONG E. ImageNet classification with deep convolutional neural networks [C]// Advances in Neural Information Processing Systems, 2012, 25: 1097-1105. |
17 | 欧阳宁,梁婷,林乐平.基于自注意力网络的图像超分辨率重建[J].计算机应用,2019,39(8):2391-2395. 10.11772/j.issn.1001-9081.2019010158 |
OUYANG N, LIANG T, LIN L P. Self-attention network based image super-resolution [J]. Journal of Computer Applications, 2019,39(8):2391-2395. 10.11772/j.issn.1001-9081.2019010158 | |
18 | CHEN L C, ZHU Y, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation [C]// Proceedings of the 2018 European Conference on Computer Vision. Cham: Springer, 2018: 801-818. 10.1007/978-3-030-01234-2_49 |
19 | 王得成,陈向宁,易辉,等.基于自适应联合双边滤波的深度图像空洞填充与优化算法[J].中国激光,2019,46(10):1009002. 10.3788/CJL201946.1009002 |
WANG D C, CHEN X N, YI H, et al. Hole filling and optimization algorithm for depth images based on adaptive joint bilateral filtering [J]. Chinese Journal of Lasers, 2019, 46(10): 1009002. 10.3788/CJL201946.1009002 | |
20 | 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]// Proceedings of the 8th IEEE International Conference on Computer Vision. Piscataway: IEEE, 2002: 416-423. 10.1109/iccv.2001.937655 |
21 | KINGMA D P, BA J L. Adam: a method for stochastic optimization [EB/OL]. [2021-03-20]. . |
22 | HE K M, ZHANG X Y, REN S Q, et al. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification [C]// Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2015: 1026-1034. 10.1109/iccv.2015.123 |
23 | LI C, YIN W, JIANG H, et al. An efficient augmented Lagrangian method with applications to total variation minimization [J]. Computational Optimization and Applications, 2013, 56(3): 507-530. 10.1007/s10589-013-9576-1 |
24 | METZLER C A, MALEKI A, BARANIUK R G. From denoising to compressed sensing [J]. IEEE Transactions on Information Theory, 2016, 62(9): 5117-5144. 10.1109/tit.2016.2556683 |
[1] | Wenqiu ZHU, Guang ZOU, Zhigao ZENG. Object tracking algorithm with hierarchical features and hybrid attention [J]. Journal of Computer Applications, 2022, 42(3): 833-843. |
[2] | Dingkang YANG, Shuai HUANG, Shunli WANG, Peng ZHAI, Yidan LI, Lihua ZHANG. EE-GAN:facial expression recognition method based on generative adversarial network and network integration [J]. Journal of Computer Applications, 2022, 42(3): 750-756. |
[3] | Tianmin DENG, Guotao MAO, Zhenhao ZHOU, Zhijian DUAN. Road vehicle detection and recognition algorithm based on densely connected convolutional neural network [J]. Journal of Computer Applications, 2022, 42(3): 883-889. |
[4] | Lu ZHANG, Chun FANG, Ming ZHU. Indoor fall detection algorithm based on Res2Net-YOLACT and fusion feature [J]. Journal of Computer Applications, 2022, 42(3): 757-763. |
[5] | Renzhi PAN, Fulan QIAN, Shu ZHAO, Yanping ZHANG. Recommendation model for user attribute preference modeling based on convolutional neural network interaction [J]. Journal of Computer Applications, 2022, 42(2): 404-411. |
[6] | Pinxue WANG, Shaobing ZHANG, Miao CHENG, Lian HE, Xiaoshan QIN. Coin surface defect detection algorithm based on deformable convolution and adaptive spatial feature fusion [J]. Journal of Computer Applications, 2022, 42(2): 638-645. |
[7] | Wei LI, Yaochi FAN, Qiaoyong JIANG, Lei WANG, Qingzheng XU. Variable convolutional autoencoder method based on teaching-learning-based optimization for medical image classification [J]. Journal of Computer Applications, 2022, 42(2): 592-598. |
[8] | Runze WANG, Yueqin ZHANG, Qiqi QIN, Zehua ZHANG, Xumin GUO. Multi-aspect multi-attention fusion of molecular features for drug-target affinity prediction [J]. Journal of Computer Applications, 2022, 42(1): 325-332. |
[9] | Hengxin LI, Kan CHANG, Yufei TAN, Mingyang LING, Tuanfa QIN. Color image demosaicking network based on inter-channel correlation and enhanced information distillation [J]. Journal of Computer Applications, 2022, 42(1): 245-251. |
[10] | Yinxin BAO, Yang CAO, Quan SHI. Improved spatio-temporal residual convolutional neural network for urban road network short-term traffic flow prediction [J]. Journal of Computer Applications, 2022, 42(1): 258-264. |
[11] | Huiqing XU, Bin CHEN, Jingfei WANG, Zhiyi CHEN, Jian QIN. Elongated pavement distress detection method based on convolutional neural network [J]. Journal of Computer Applications, 2022, 42(1): 265-272. |
[12] | SONG Zhongshan, LIANG Jiarui, ZHENG Lu, LIU Zhenyu, TIE Jun. Remote sensing scene classification based on bidirectional gated scale feature fusion [J]. Journal of Computer Applications, 2021, 41(9): 2726-2735. |
[13] | LI Kangkang, ZHANG Jing. Multi-layer encoding and decoding model for image captioning based on attention mechanism [J]. Journal of Computer Applications, 2021, 41(9): 2504-2509. |
[14] | ZHANG Yongbin, CHANG Wenxin, SUN Lianshan, ZHANG Hang. Detection method of domains generated by dictionary-based domain generation algorithm [J]. Journal of Computer Applications, 2021, 41(9): 2609-2614. |
[15] | ZHAO Hong, KONG Dongyi. Chinese description of image content based on fusion of image feature attention and adaptive attention [J]. Journal of Computer Applications, 2021, 41(9): 2496-2503. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||