{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T18:39:17Z","timestamp":1761676757672,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,6,4]],"date-time":"2019-06-04T00:00:00Z","timestamp":1559606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The existing compressive sensing (CS) reconstruction algorithms require enormous computation and reconstruction quality that is not satisfying. In this paper, we propose a novel Dual-Channel Reconstruction Network (DC-Net) module to build two CS reconstruction networks: the first one recovers an image from its traditional random under-sampling measurements (RDC-Net); the second one recovers an image from its CS measurements acquired by a fully connected measurement matrix (FDC-Net). Especially, the fully connected under-sampling method makes CS measurements represent original images more effectively. For the two proposed networks, we use a fully connected layer to recover a preliminary reconstructed image, which is a linear mapping from CS measurements to the preliminary reconstructed image. The DC-Net module is used to further improve the preliminary reconstructed image quality. In the DC-Net module, a residual block channel can improve reconstruction quality and dense block channel can expedite calculation, whose fusion can improve the reconstruction performance and reduce runtime simultaneously. Extensive experiments manifest that the two proposed networks outperform state-of-the-art CS reconstruction methods in PSNR and have excellent visual reconstruction effects.<\/jats:p>","DOI":"10.3390\/s19112549","type":"journal-article","created":{"date-parts":[[2019,6,5]],"date-time":"2019-06-05T09:37:58Z","timestamp":1559727478000},"page":"2549","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Dual-Channel Reconstruction Network for Image Compressive Sensing"],"prefix":"10.3390","volume":"19","author":[{"given":"Zhongqiang","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dahua","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuemei","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangming","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","article-title":"Compressed sensing","volume":"52","author":"Donoho","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Huang, G., Jiang, H., Matthews, K., and Wilford, P. (2013, January 15\u201318). Lensless imaging by compressive sensing. Proceedings of the 2013 IEEE International Conference on Image Processing, Melbourne, Australia.","DOI":"10.1109\/ICIP.2013.6738433"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"14013","DOI":"10.1364\/OE.15.014013","article-title":"Single-shot compressive spectral imaging with a dual-disperser architecture","volume":"15","author":"Gehm","year":"2007","journal-title":"Opt. Express"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1137\/120875302","article-title":"Coded Hyperspectral Imaging and Blind Compressive Sensing","volume":"6","author":"Rajwade","year":"2013","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Hitomi, Y., Gu, J., Gupta, M., Mitsunaga, T., and Nayar, S.K. (2011, January 6\u201313). Video from a single coded exposure photograph using a learned over-complete dictionary. Proceedings of the International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126254"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/MSP.2007.914728","article-title":"Compressed Sensing MRI","volume":"25","author":"Lustig","year":"2008","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3397","DOI":"10.1109\/78.258082","article-title":"Matching pursuits with time-frequency dictionaries","volume":"41","author":"Mallat","year":"1993","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4655","DOI":"10.1109\/TIT.2007.909108","article-title":"Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit","volume":"53","author":"Tropp","year":"2007","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2231","DOI":"10.1109\/TIT.2004.834793","article-title":"Greed is good: Algorithmic results for sparse approximation","volume":"50","author":"Tropp","year":"2004","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1007\/s10589-013-9576-1","article-title":"An efficient augmented Lagrangian method with applications to total variation minimization","volume":"56","author":"Li","year":"2013","journal-title":"Comput. Optim. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.acha.2009.04.002","article-title":"Iterative hard thresholding for compressed sensing","volume":"27","author":"Blumensath","year":"2009","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1002\/cpa.20042","article-title":"An iterative thresholding algorithm for linear inverse problems with a sparsity constraint","volume":"57","author":"Daubechies","year":"2003","journal-title":"Commun. Pure Appl. Math."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"18914","DOI":"10.1073\/pnas.0909892106","article-title":"Message-passing algorithms for compressed sensing","volume":"106","author":"Donoho","year":"2009","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5117","DOI":"10.1109\/TIT.2016.2556683","article-title":"From Denoising to Compressed Sensing","volume":"62","author":"Metzler","year":"2016","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mousavi, A., Patel, A.B., and Baraniuk, R.G. (October, January 29). A deep learning approach to structured signal recovery. Proceedings of the 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, USA.","DOI":"10.1109\/ALLERTON.2015.7447163"},{"key":"ref_16","unstructured":"Kulkarni, K., Lohit, S., Turaga, P.K., Kerviche, R., and Ashok, A. (July, January 26). ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Random Measurements. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_17","unstructured":"Yao, H., Dai, F., Zhang, D., Ma, Y., Zhang, S., and Zhang, Y. (2017, January 21\u201326). DR2-Net: Deep Residual Reconstruction Network for Image Compressive Sensing. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mousavi, A., and Baraniuk, R.G. (2017, January 5\u20139). Learning to Invert: Signal Recovery via Deep Convolutional Networks. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7952561"},{"key":"ref_19","unstructured":"Metzler, C., Mousavi, A., and Baraniuk, R. (2017, January 4\u20139). Learned D-AMP: Principled Neural Network based Compressive Image Recovery. Proceedings of the 30th International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.jvcir.2017.01.028","article-title":"Block compressive sensing: Individual and joint reconstruction of correlated images","volume":"44","author":"Unde","year":"2017","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_21","unstructured":"Mun, S., and Fowler, J.E. (2009, January 7\u201310). Block Compressed Sensing of Images Using Directional Transforms. Proceedings of the 2009 16th IEEE International Conference on Image Processing (ICIP), Cairo, Egypt."},{"key":"ref_22","unstructured":"Huang, G., Liu, Z., and Weinberger, K.Q. (July, January 26). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_23","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_24","unstructured":"Lu, X., Dong, W., Wang, P., Shi, G., and Xie, X. (2018). ConvCSNet: A Convolutional Compressive Sensing Framework Based on Deep Learning. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1186\/s13640-018-0315-5","article-title":"Cascaded reconstruction network for compressive image sensing","volume":"2018","author":"Wang","year":"2018","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.acha.2011.03.004","article-title":"An iterative thresholding algorithm for linear inverse problems with multi-constraints and its applications","volume":"32","author":"Khoramian","year":"2012","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2085","DOI":"10.1109\/TSP.2015.2408558","article-title":"Compressive Imaging via Approximate Message Passing with Image Denoising","volume":"63","author":"Jin","year":"2015","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1109\/JSTSP.2015.2500190","article-title":"Compressive Hyperspectral Imaging via Approximate Message Passing","volume":"10","author":"Tan","year":"2016","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_29","unstructured":"Schniter, P., Rangan, S., and Fletcher, A. (2016). Denoising based Vector Approximate Message Passing. arXiv."},{"key":"ref_30","unstructured":"Tipping, M.E., and Faul, A. (2003, January 3\u20136). Fast Marginal Likelihood Maximisation for Sparse Bayesian Models. Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Key West, FL, USA."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wu, J., Liu, F., and Jiao, L. (2011, January 15\u201317). Fast lp-sparse Bayesian learning for compressive sensing reconstruction. Proceedings of the 2011 4th International Congress on Image and Signal Processing, Shanghai, China.","DOI":"10.1109\/CISP.2011.6100618"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1109\/LSP.2017.2789163","article-title":"A Unified Bayesian Inference Framework for Generalized Linear Models","volume":"25","author":"Meng","year":"2018","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"7965","DOI":"10.1109\/ACCESS.2018.2890146","article-title":"An AMP-Based Low Complexity Generalized Sparse Bayesian Learning Algorithm","volume":"7","author":"Zhu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2920","DOI":"10.1109\/TIP.2016.2556582","article-title":"Two-Dimensional Pattern-Coupled Sparse Bayesian Learning via Generalized Approximate Message Passing","volume":"25","author":"Fang","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Shekaramiz, M., Moon, T.K., and Gunther, J.H. (2019). Bayesian Compressive Sensing of Sparse Signals with Unknown Clustering Patterns. Entropy, 21.","DOI":"10.3390\/e21030247"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1049\/iet-ipr.2012.0543","article-title":"Fusion framework for multi-focus images based on compressed sensing","volume":"7","author":"Kang","year":"2013","journal-title":"IET Image Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/TSP.2012.2229994","article-title":"Convolutional Compressed Sensing Using Deterministic Sequences","volume":"61","author":"Li","year":"2013","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1109\/LSP.2014.2311659","article-title":"Convolutional Compressed Sensing Using Decimated Sidelnikov Sequences","volume":"21","author":"Yu","year":"2014","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1016\/j.jvcir.2013.06.019","article-title":"A learning-based method for compressive image recovery","volume":"24","author":"Dong","year":"2013","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1109\/TMI.2017.2785879","article-title":"DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction","volume":"37","author":"Yang","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_41","unstructured":"Yu, S., Dong, H., Yang, G., Slabaugh, G.G., Dragotti, P.L., Ye, X., Liu, F., Arridge, S.R., Keegan, J., and Firmin, D.N. (2017). Deep De-Aliasing for Fast Compressive Sensing MRI. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Seitzer, M., Yang, G., Schlemper, J., Oktay, O., W\u00fcrfl, T., Christlein, V., Wong, T., Mohiaddin, R., Firmin, D., and Keegan, J. (2018, January 16\u201320). Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention\u2014MICCAI, Granada, Spain.","DOI":"10.1007\/978-3-030-00928-1_27"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Schlemper, J., Yang, G., Ferreira, P., Scott, A., McGill, L.A., Khalique, Z., Gorodezky, M., Roehl, M., Keegan, J., and Pennell, D. (2018, January 16\u201320). Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI. Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2014MICCAI, Granada, Spain.","DOI":"10.1007\/978-3-030-00928-1_34"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","article-title":"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising","volume":"26","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_45","first-page":"245","article-title":"Improvement of Gaussian Random Measurement Matrices in Compressed Sensing","volume":"301\u2013303","author":"Wang","year":"2011","journal-title":"Adv. Mater. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1109\/LSP.2016.2550101","article-title":"A Note on Compressed Sensing of Structured Sparse Wavelet Coefficients From Subsampled Fourier Measurements","volume":"23","author":"Adcock","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Huang, T., Fan, Y.Z., and Hu, M. (2017, January 19\u201321). Compressed sensing based on random symmetric Bernoulli matrix. Proceedings of the 2017 32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC), Hefei, China.","DOI":"10.1109\/YAC.2017.7967403"},{"key":"ref_48","unstructured":"Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1988). Learning Representations by Back-Propagating Errors, MIT Press."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2080","DOI":"10.1109\/TIP.2007.901238","article-title":"Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering","volume":"16","author":"Dabov","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014, January 3\u20137). Caffe: Convolutional Architecture for Fast Feature Embedding. Proceedings of the 22nd ACM International Conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3618","DOI":"10.1109\/TIP.2014.2329449","article-title":"Compressive Sensing via Nonlocal Low-Rank Regularization","volume":"23","author":"Dong","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.-F. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/11\/2549\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:56:08Z","timestamp":1760187368000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/11\/2549"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,4]]},"references-count":52,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["s19112549"],"URL":"https:\/\/doi.org\/10.3390\/s19112549","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,6,4]]}}}