{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:55:55Z","timestamp":1760241355125,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,1,5]],"date-time":"2018-01-05T00:00:00Z","timestamp":1515110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Nature Science Foundation of China","award":["61672335","61602191"],"award-info":[{"award-number":["61672335","61602191"]}]},{"name":"Foundation of Fujian Education Department under Grand","award":["JAT170053"],"award-info":[{"award-number":["JAT170053"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Due to the limitations of the resolution of the imaging system and the influence of scene changes and other factors, sometimes only low-resolution images can be acquired, which cannot satisfy the practical application\u2019s requirements. To improve the quality of low-resolution images, a novel super-resolution algorithm based on an improved sparse autoencoder is proposed. Firstly, in the training set preprocessing stage, the high- and low-resolution image training sets are constructed, respectively, by using high-frequency information of the training samples as the characterization, and then the zero-phase component analysis whitening technique is utilized to decorrelate the formed joint training set to reduce its redundancy. Secondly, a constructed sparse regularization term is added to the cost function of the traditional sparse autoencoder to further strengthen the sparseness constraint on the hidden layer. Finally, in the dictionary learning stage, the improved sparse autoencoder is adopted to achieve unsupervised dictionary learning to improve the accuracy and stability of the dictionary. Experimental results validate that the proposed algorithm outperforms the existing algorithms both in terms of the subjective visual perception and the objective evaluation indices, including the peak signal-to-noise ratio and the structural similarity measure.<\/jats:p>","DOI":"10.3390\/info9010011","type":"journal-article","created":{"date-parts":[[2018,1,8]],"date-time":"2018-01-08T04:21:21Z","timestamp":1515385281000},"page":"11","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Image Super-Resolution Algorithm Based on an Improved Sparse Autoencoder"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8542-3728","authenticated-orcid":false,"given":"Detian","family":"Huang","sequence":"first","affiliation":[{"name":"College of Engineering, Huaqiao University, No. 269, Chenghuabei Road, Quanzhou 362021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiqin","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Engineering, Huaqiao University, No. 269, Chenghuabei Road, Quanzhou 362021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenguo","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Shantou University, No. 243, Daxue Road, Shantou 515063, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanming","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Engineering, Huaqiao University, No. 269, Chenghuabei Road, Quanzhou 362021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Engineering, Huaqiao University, No. 269, Chenghuabei Road, Quanzhou 362021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lixin","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Engineering, Huaqiao University, No. 269, Chenghuabei Road, Quanzhou 362021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,5]]},"reference":[{"key":"ref_1","first-page":"209","article-title":"Single image super resolution based on adaptive multi-dictionary learning","volume":"43","author":"Pan","year":"2015","journal-title":"Acta Electron. Sin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1007\/s11801-017-7143-1","article-title":"Application of regularization technique in image super-resolution algorithm via sparse representation","volume":"13","author":"Huang","year":"2017","journal-title":"Optoelectron. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3311","DOI":"10.1016\/S0042-6989(97)00169-7","article-title":"Sparse coding with an overcomplete basis set: A strategy employed by V1?","volume":"37","author":"Olshausen","year":"1997","journal-title":"Vis. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1038\/44565","article-title":"Learning the parts of objects by non-negative matrix factorization","volume":"401","author":"Lee","year":"1999","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3736","DOI":"10.1109\/TIP.2006.881969","article-title":"Image denoisingvia sparse and redundant representations over learned dictionaries","volume":"15","author":"Elad","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Koh, M.S., and Rodriguez-Marek, E. (2009, January 5\u20137). Turbo inpainting: Iterative K-SVD with a new dictionary. Proceedings of the IEEE International Workshop on Multimedia Signal Processing, Rio De Janeiro, Brazil.","DOI":"10.1109\/MMSP.2009.5293571"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/TIP.2007.911828","article-title":"Sparse representation for color image restoration","volume":"17","author":"Mairal","year":"2008","journal-title":"IEEE Trans. Image Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2542","DOI":"10.1109\/TIP.2014.2319732","article-title":"Local learned dictionaries optimized to edge orientation for inverse halftoning","volume":"23","author":"Son","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1109\/TMI.2014.2301271","article-title":"Dictionary learning and time sparsity for dynamic MR data reconstruction","volume":"33","author":"Caballero","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Majumdar, A., and Ward, R. (2015, January 27\u201330). Learning space-time dictionaries for blind compressed sensing dynamic MRI reconstruction. Proceedings of the IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7351668"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1951","DOI":"10.1016\/j.neucom.2008.05.001","article-title":"Feature extraction using Radon and wavelet transforms with application to face recognition","volume":"72","author":"Jadhav","year":"2009","journal-title":"Neurocomputing"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1016\/j.patcog.2009.11.001","article-title":"Feature extraction using discrete cosine transform and discrimination power analysis with a face recognition technology","volume":"43","author":"Dabbaghchian","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_13","unstructured":"Engan, K., Aase, S.O., and Husoy, J.H. (June, January 30). Frame based signal compression using method of optimal directions (MOD). Proceedings of the IEEE International Symposium on Circuits and Systems, Orlando, FL, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4311","DOI":"10.1109\/TSP.2006.881199","article-title":"rm K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation","volume":"54","author":"Aharon","year":"2006","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_15","first-page":"19","article-title":"Online learning for matrix factorization and sparse coding","volume":"11","author":"Mairal","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Singhal, V., Gogna, A., and Majumdar, A. (2016, January 16\u201321). Deep dictionary learning vs deep belief network vs stacked autoencoder: An empirical analysis. Proceedings of the International Conference on Neural Information Processing, Kyoto, Japan.","DOI":"10.1007\/978-3-319-46681-1_41"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2861","DOI":"10.1109\/TIP.2010.2050625","article-title":"Image super-resolution via sparse representation","volume":"19","author":"Yang","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_18","unstructured":"Zeyde, R., Elad, M., and Protter, M. (2010, January 24\u201330). On single image scale-up using sparse-representations. Proceedings of the International Conference on Curves and Surfaces, Avignon, France."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1109\/TIP.2017.2651364","article-title":"Super-resolution person re-identification with semi-coupled low-rank discriminant dictionary learning","volume":"26","author":"Jing","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, W., and Duan, Z. (2016, January 13\u201316). Image super-resolution reconstruction based on fusion of K-SVD and semi-coupled dictionary learning. Proceedings of the Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), Jeju, Korea.","DOI":"10.1109\/APSIPA.2016.7820691"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Liu, Y. (2017). Single image super-resolution reconstruction method based on LC-KSVD algorithm. AIP Conf. Proc., 1521\u20131527.","DOI":"10.1063\/1.4982460"},{"key":"ref_22","first-page":"406","article-title":"Retrieval of remote sensing images based on semisupervised deep learning","volume":"21","author":"Zhang","year":"2017","journal-title":"J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1002\/cpa.20132","article-title":"For most large underdetermined systems of equations, the minimal \u21131-norm near-solution approximates the sparsest near-solution","volume":"59","author":"Donoho","year":"2010","journal-title":"Commun. Pure Appl. Math."},{"key":"ref_24","first-page":"1037","article-title":"SPCA: Sparse principal component analysis","volume":"34","author":"Merola","year":"2014","journal-title":"Pattern Recognit. Lett."},{"key":"ref_25","unstructured":"Krizhevsky, A. (2009). Learning Multiple Layers of Features from Tiny Images, University of Toronto. Technical Report."},{"key":"ref_26","unstructured":"Ng, A. (2011). Sparse Autoencoder, Stanford University. CS294A Lecture Notes."},{"key":"ref_27","unstructured":"Miller, F.P., Vandome, A.F., and Mcbrewster, J. (2010). Gradient Descent, Alphascript Publishing."},{"key":"ref_28","first-page":"22","article-title":"Single image super-resolution based on the feature sign method","volume":"44","author":"Li","year":"2015","journal-title":"J. Univ. Electron. Sci. Technol. China"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.infrared.2017.04.006","article-title":"Image super-resolution reconstruction based on regularization technique and guided filter","volume":"83","author":"Huang","year":"2017","journal-title":"Infrared Phys. Technol."},{"key":"ref_30","unstructured":"Wang, Y., Li, J., Lu, Y., and Fu, Y. (2003, January 14\u201317). Image quality evaluation based on image weighted separating block peak signal to noise ratio. Proceedings of the IEEE International Conference on Neural Networks and Signal Processing, Nanjing, China."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Bevilacqua, M., Roumy, A., Guillemot, C., and Morel, A. (2012, January 3\u20137). Low-complexity single-image super-resolution based on nonnegative neighbor embedding. Proceedings of the British Machine Vision Conference, Guildford, UK.","DOI":"10.5244\/C.26.135"},{"key":"ref_33","unstructured":"Martin, D., Fowlkes, C., Tal, D., and Malik, J. (2001, January 7\u201314). A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. Proceedings of the IEEE International Conference on Computer Vision, Vancouver, BC, Canada."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Timofte, R., De, V., and Gool, L.V. (2013, January 1\u20138). Anchored neighborhood regression for fast example-based super-resolution. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney, Australia.","DOI":"10.1109\/ICCV.2013.241"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Timofte, R., Smet, V.D., and Gool, L.V. (2014). A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution. Asian Conference on Computer Vision, Springer.","DOI":"10.1109\/ICCV.2013.241"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","article-title":"The Cancer Imaging Archive (TCIA): Maintaining and operating a public information repository","volume":"26","author":"Clark","year":"2013","journal-title":"J. Digit. Imaging"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"828","DOI":"10.11834\/jig.200407155","article-title":"Research of measurement for Digital Image Definition","volume":"9","author":"Wang","year":"2004","journal-title":"J. Image Graph."},{"key":"ref_38","first-page":"239","article-title":"Research of Definition Assessment based on No-reference Digital Image Quality","volume":"26","author":"Li","year":"2011","journal-title":"Remote Sens. Technol. 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