{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:36:36Z","timestamp":1777703796506,"version":"3.51.4"},"reference-count":17,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2019,7,2]],"date-time":"2019-07-02T00:00:00Z","timestamp":1562025600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2019,10,25]]},"abstract":"<jats:p>Measurement matrix is an important link which has important influence on signal sampling and reconstruction algorithm. Although the traditional random measurement matrix has good effect in reconstruction signal, the hardware implementation is difficult and it requires a lot of storage space. LDPC codes has a sparse check matrix with low density and strong orthogonality. When the RIP conditions are satisfied, the columns and rows are not correlated. In view of the problems existing in the measurement matrix, the deterministic measurement matrix based on the sparsity of photograph LDPC codes is constructed in this paper. Each submatrix is obtained through the circular shift of other submatrices, which is easy to be implemented on hardware. Experimental results show that the sparse matrix performance of LDPC codes is improved in PSNR and dNMSE compared to the traditional methods by using the same orthogonal matching pursuit (OMP) algorithm for optimization and the same compression ratio. At the same time, it consumes less time in the reconstruction of remote sensing image, and the running speed is greatly improved, which can meet the real-time demand, it also provides an effective measurement matrix construction method for the practical application of compressed sensing theory.<\/jats:p>","DOI":"10.3233\/jifs-179294","type":"journal-article","created":{"date-parts":[[2019,7,2]],"date-time":"2019-07-02T10:42:40Z","timestamp":1562064160000},"page":"4605-4613","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Compressed sensing of remote image based on photograph LDPC codes matrix"],"prefix":"10.1177","volume":"37","author":[{"given":"Xiaoqi","family":"Yin","sequence":"first","affiliation":[{"name":"School of Informatiom and Electrical Engineering, China University of Mining and Technology, Xuzhou Jiangsu, China"},{"name":"Jiangsu Laboratory of Lake Environment Remote Sensing Technologies, Huaiyin Institute of Technology, Huaian Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiansheng","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Informatiom and Electrical Engineering, China University of Mining and Technology, Xuzhou Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingge","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Informatiom and Electrical Engineering, China University of Mining and Technology, Xuzhou Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengfang","family":"Fu","sequence":"additional","affiliation":[{"name":"Jiangsu Laboratory of Lake Environment Remote Sensing Technologies, Huaiyin Institute of Technology, Huaian Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guohua","family":"Lin","sequence":"additional","affiliation":[{"name":"Jiangsu Laboratory of Lake Environment Remote Sensing Technologies, Huaiyin Institute of Technology, Huaian Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2019,7,2]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"225","article-title":"Survey on reconstruction algorithm based on compressive sensing","volume":"42","author":"Shen Li","year":"2013","unstructured":"ShenLi, CaiwenMa, YanLi, et al., Survey on reconstruction algorithm based on compressive sensing, Infrared and Laser Engineering 42 (2013), 225\u2013232.","journal-title":"Infrared and Laser Engineering"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jco.2007.04.002"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.crma.2008.03.014"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10208-008-9031-3"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2010.2042412"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICINFA.2010.5512379"},{"key":"e_1_3_2_8_2","first-page":"1","article-title":"Circulant and Toeplitz matrices in compressed sensing","author":"Rauhut H.","year":"2009","unstructured":"RauhutH., Circulant and Toeplitz matrices in compressed sensing, Mathematics (2009), 1\u20136. doi: http:\/\/dx.doi.org\/","journal-title":"Mathematics"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/tit.2010.2070191"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2005.858979"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00365-007-9003-x"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2010.2054653"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2011.2181819"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1981.1056404"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2017.2749043"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2015.2447934"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2017.2677965"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2016.2569579"}],"container-title":["Journal of Intelligent &amp; 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