1791c0fc-2974-49d0-bac0-445e26a41ebf20210604083403195naun:naunmdt@crossref.orgMDT DepositInternational Journal of Circuits, Systems and Signal Processing1998-446410.46300/9106http://www.naun.org/cms.action?id=3029118202111820211510.46300/9106.2021.15https://naun.org/cms.action?id=23283An Image Reconstruction Algorithm based on Sparse Representation for Image Compressed SensingShuyaoTianCollege of Electronics and Control Engineering, North China Institute of Aerospace Engineering, Langfang, ChinaLianchengZhangCollege of Electronics and Control Engineering, North China Institute of Aerospace Engineering, Langfang, ChinaYajunLiuCollege of Mechanical and Electrical Engineering, Hebei Normal University of Science & Technology, Qinhuangdao, ChinaIt is difficult to control the balance between artifact suppression and detail preservation. In addition, the information contained in the reconstructed image is limited. For achieving the purpose of less lost information and lower computational complexity in the sampling process, this paper proposed a novel algorithm to realize the image reconstruction using sparse representation. Firstly, the principle of algorithm for sparse representation is introduced, and then the current commonly used reconstruction algorithms are described in detail. Finally, the algorithm can still process the image when the sparsity is unknown by introducing the sparsity theory and dynamically changing the step size to approximate the sparsity. The results explain that the improved algorithm can not only reconstruct the image with unknown sparsity, but also has advantages over other algorithms in reconstruction time. In addition, compared with other algorithms, the reconstruction time of the improved algorithm is the shortest under the same sampling rate.642021642021511518https://www.naun.org/main/NAUN/circuitssystemssignal/2021/b142005-056(2021).pdf10.46300/9106.2021.15.56https://www.naun.org/main/NAUN/circuitssystemssignal/2021/b142005-056(2021).pdf10.1016/j.neucom.2018.12.087He N A , Wang R B , Wang Y C . “Dynamic MRI reconstruction exploiting blind compressed sensing combined transform learning regularization - ScienceDirect”, Neurocomputing, 2020, 392, pp. 160-167.10.1186/s12968-019-0582-zPeper E S, Gottwald L M, Zhang Q, et al. “Highly accelerated 4D flow cardiovascular magnetic resonance using a pseudo-spiral Cartesian acquisition and compressed sensing reconstruction for carotid flow and wall shear stress”, Journal of Cardiovascular Magnetic Resonance, 2020, 22, pp. 2278-2324.10.1007/s12065-020-00475-9Kavitha T S , Prasad K S . “Hybridizing ant lion with whale optimization algorithm for compressed sensing MR image reconstruction via l1 minimization: an ALWOA strategy”, Evolutionary Intelligence, 2020, 7, pp.1-11.10.1097/rct.0000000000001029Onodera M , K Aratani, Shonai T , et al. “Lateral Position With Gantry Tilt Further Improves Computed Tomography Image Quality Reconstructed Using Single-Energy Metal Artifact Reduction Algorithm in the Oral Cavity”, Journal of Computer Assisted Tomography, 2020, 44, pp. 553-558.10.7555/jbr.34.20190043N Ilakiyaselvan, AN Khan, A Shahina, “Deep learning approach to detect seizure using reconstructed phase space images”, Journal of Biomedical Research, 2020, 34, pp. 238-249.10.1002/acm2.12821Narita A , Ohkubo M . “A pitfall of using the circular‐edge technique with image averaging for spatial resolution measurement in iteratively reconstructed CT images”, Journal of Applied Clinical Medical Physics, 2020, 21, pp. 77-90.10.1016/j.mrgentox.2021.503314Allemang A , Thacker R , Demarco R A , et al. “The 3D reconstructed skin micronucleus assay using imaging flow cytometry and deep learning: a proof-of-principle investigation”, Mutation Research/Genetic Toxicology and Environmental Mutagenesis, 2021:503314.10.1016/j.radonc.2020.10.004Dhont, Verellen D , Mollaert I , et al. “RealDRR – Rendering of realistic digitally reconstructed radiographs using locally trained image-to-image translation”, Radiotherapy and Oncology, 2020, 153, pp. 352-360.Zhao, Huang, Liu, et al. “A sparse representation-based fusion model for improving daily MODIS C6.1 aerosol products on a 3 km grid”, International Journal of Remote Sensing, 2021 285, pp. 204-219.10.1016/j.measurement.2021.109104Yang J , Tse P . “Sparse representation of complex steerable pyramid for machine fault diagnosis by using non-contact video motion to replace conventional accelerometers”, Measurement, 2021, 365:109104.10.1155/2020/8964321Zheng S , Zhang Y , Liu W , et al. A Dictionary Learning Algorithm Based on Dictionary Reconstruction and Its Application in Face Recognition. Mathematical Problems in Engineering, 2020, 2020, pp.1-13.10.1109/access.2020.2996303Wang G , Han H , Carranza E , et al. Tensor-Based Low-Rank and Sparse Prior Information Constraints for Hyperspectral Image Denoising. IEEE Access, 2020, PP(99):1-1.10.1109/jstsp.2021.3051746Li B, Rencker L, Dong J , et al. Sparse Analysis Model Based Dictionary Learning for Signal Declipping. IEEE Journal of Selected Topics in Signal Processing, 2021, PP(99):1-1.10.1016/j.bspc.2019.101810Maqsood S , Javed U . Multi-modal Medical Image Fusion based on Two-scale Image Decomposition and Sparse Representation. Biomedical Signal Processing and Control, 2020, 57:101810.10.1002/cpa.21850Huynh T , Saab R . Fast binary embeddings, and quantized compressed sensing with structured matrices. Communications on Pure and Applied Mathematics, 2020, 73, pp. 231-244.10.1109/igarss.2018.8518227Yu H , Gao L , Liao W , et al. Global Spatial and Local Spectral Similarity-Based Manifold Learning Group Sparse Representation for Hyperspectral Imagery Classification. IEEE Transactions on Geoscience and Remote Sensing, 2020, 121, 854-863.10.37394/23205.2020.19.2Rafeek Mamdouh, Hazem M. El-Bakry, Alaa Riad, Nashaat El-Khamisy, Converting 2D-Medical Image Files “DICOM” into 3D- Models, based on Image Processing, and Analysing their Results with Python Programming, WSEAS Transactions on Computers, Volume 19, 2020, Art. #2, pp. 10-20.