{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T07:54:54Z","timestamp":1777362894325,"version":"3.51.4"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2020,9,21]],"date-time":"2020-09-21T00:00:00Z","timestamp":1600646400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,9,21]],"date-time":"2020-09-21T00:00:00Z","timestamp":1600646400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper proposes a low-complexity convolutional neural network (CNN) for super-resolution (SR). The proposed deep-learning model for SR has two layers to deal with horizontal, vertical, and diagonal visual information. The front-end layer extracts the horizontal and vertical high-frequency signals using a CNN with one-dimensional (1D) filters. In the high-resolution image-restoration layer, the high-frequency signals in the diagonal directions are processed by additional two-dimensional (2D) filters. The proposed model consists of 1D and 2D filters, and as a result, we can reduce the computational complexity of the existing SR algorithms, with negligible visual loss. The computational complexity of the proposed algorithm is 71.37%, 61.82%, and 50.78% lower in CPU, TPU, and GPU than the very-deep SR (VDSR) algorithm, with a peak signal-to-noise ratio loss of 0.49\u00a0dB.<\/jats:p>","DOI":"10.1007\/s11554-020-01019-1","type":"journal-article","created":{"date-parts":[[2020,9,21]],"date-time":"2020-09-21T05:02:43Z","timestamp":1600664563000},"page":"2065-2076","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Low-complexity CNN with 1D and 2D filters for super-resolution"],"prefix":"10.1007","volume":"17","author":[{"given":"Jangsoo","family":"Park","sequence":"first","affiliation":[]},{"given":"Jongseok","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Donggyu","family":"Sim","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,21]]},"reference":[{"key":"1019_CR1","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/MSP.2003.1203207","volume":"20","author":"SC Park","year":"2003","unstructured":"Park, S.C., Park, M.K., Kang, M.G.: Super-resolution image reconstruction: a technical overview. IEEE Signal Process. Mag. 20, 21\u201336 (2003)","journal-title":"IEEE Signal Process. Mag."},{"key":"1019_CR2","first-page":"1303","volume":"15","author":"ME Tipping","year":"2003","unstructured":"Tipping, M.E., Bishop, C.M.: Bayesian image super-resolution. Adv. Neural Inf. Process. Syst. 15, 1303\u20131310 (2003)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1019_CR3","unstructured":"Tian, J., Ma, K.-K.: An MCMC approach for Bayesian super-resolution image reconstruction. In: IEEE Int. Conf. Image Process., Genova (2005)"},{"key":"1019_CR4","unstructured":"Shimizu, M., Yoshimura, S., Tanaka, M., Okutomi, M.: Super-resolution from image sequence under influence of hot-air optical turbulence. In: 2008 IEEE Conf. Comput. Vis. Pattern Recognit., Anchorage (2008)"},{"key":"1019_CR5","doi-asserted-by":"publisher","first-page":"271","DOI":"10.5573\/IEIESPC.2014.3.5.271","volume":"3","author":"OY Lee","year":"2014","unstructured":"Lee, O.Y., Park, S.J., Kim, J.W., Kim, J.O.: Multi-frame super-resolution of high frequency with spatially weighted bilateral total variance regularization. IEIE Trans. Smart Process. Comput. 3, 271\u2013274 (2014)","journal-title":"IEIE Trans. Smart Process. Comput."},{"key":"1019_CR6","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1109\/38.988747","volume":"22","author":"W Freeman","year":"2002","unstructured":"Freeman, W., Jones, T., Pasztor, E.: Example-based super-resolution. IEEE Comput. Graph. Appl. 22, 56\u201365 (2002)","journal-title":"IEEE Comput. Graph. Appl."},{"key":"1019_CR7","doi-asserted-by":"publisher","first-page":"2861","DOI":"10.1109\/TIP.2010.2050625","volume":"19","author":"J Yang","year":"2010","unstructured":"Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19, 2861\u20132873 (2010)","journal-title":"IEEE Trans. Image Process."},{"key":"1019_CR8","doi-asserted-by":"crossref","unstructured":"Timofte, R., Smet, V.D., Gool, L.V.: A+: adjusted anchored neighborhood regression for fast super-resolution. In: Asian Conf. Comput. Vis. (ACCV 2014), Singapore (2014)","DOI":"10.1007\/978-3-319-16817-3_8"},{"key":"1019_CR9","doi-asserted-by":"crossref","unstructured":"Huang, J.-B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: 2015 IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Boston (2015)","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"1019_CR10","doi-asserted-by":"publisher","first-page":"10","DOI":"10.5573\/IEIESPC.2015.4.1.010","volume":"4","author":"JH Jun","year":"2015","unstructured":"Jun, J.H., Choi, J.H., Lee, D.Y., Jeong, S., Cho, S.H., Kim, H.Y., Kim, J.O.: Accelerating self-similarity-based image super-resolution using OpenCL. IEIE Trans. Smart Process. Comput. 4, 10\u201315 (2015)","journal-title":"IEIE Trans. Smart Process. Comput."},{"key":"1019_CR11","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/s11554-018-0774-z","volume":"16","author":"Y Yuan","year":"2019","unstructured":"Yuan, Y., Yang, X., Wu, W., Li, H., Liu, Y., Liu, K.: A fast single-image super-resolution method implemented with CUDA. J. Real Time Image Process. 16, 81\u201397 (2019)","journal-title":"J. Real Time Image Process."},{"key":"1019_CR12","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1007\/s11554-015-0513-7","volume":"14","author":"C Jung","year":"2018","unstructured":"Jung, C., Ke, P., Sun, Z., Gu, A.: A fast deconvolution-based approach for single image super-resolution with GPU acceleration. J. Real Time Image Process. 14, 501\u2013512 (2018)","journal-title":"J. Real Time Image Process."},{"key":"1019_CR13","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Jia, D., Hao, S., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Li, F.-F.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vis."},{"key":"1019_CR14","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1109\/MSP.2017.2739299","volume":"34","author":"MT McCann","year":"2017","unstructured":"McCann, M.T., Jin, K.H., Unser, M.: Convolutional neural networks for inverse problems in imaging: a review. IEEE Sig. Process. Mag. 34, 85\u201395 (2017)","journal-title":"IEEE Sig. Process. Mag."},{"key":"1019_CR15","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/MSP.2017.2760358","volume":"35","author":"A Lucas","year":"2018","unstructured":"Lucas, A., Iliadis, M., Molina, R., Katsaggelos, A.K.: Using deep neural networks for inverse problems in imaging: Beyond analytical methods. IEEE Signal Process. Mag. 35, 20\u201336 (2018)","journal-title":"IEEE Signal Process. Mag."},{"key":"1019_CR16","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2016","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38, 295\u2013307 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1019_CR17","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: 2016 IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Las Vegas (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"1019_CR18","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., Tang. X.: Accelerating the super-resolution convolutional neural network. In: European Conf. on Comput. Vis. (ECCV). Springer (2016)","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"1019_CR19","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: 2016 IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Las Vegas (2016)","DOI":"10.1109\/CVPR.2016.207"},{"key":"1019_CR20","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.imavis.2019.03.006","volume":"88","author":"P Shamsolmoali","year":"2019","unstructured":"Shamsolmoali, P., Zareapoor, M., Zhang, J., Yang, J.: Image super resolution by dilated dense progressive network. Image Vis. Comput. 88, 9\u201318 (2019)","journal-title":"Image Vis. Comput."},{"key":"1019_CR21","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.image.2019.08.008","volume":"79","author":"P Shamsolmoali","year":"2019","unstructured":"Shamsolmoali, P., Li, X., Wang, R.: Single image resolution enhancement by efficient dilated densely connected residual network. Signal Process. Image Commun. 79, 13\u201323 (2019)","journal-title":"Signal Process. Image Commun."},{"key":"1019_CR22","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.neucom.2019.07.094","volume":"366","author":"P Shamsolmoali","year":"2019","unstructured":"Shamsolmoali, P., Zareapoor, M., Wang, R., Jain, D.K., Yang, J.: G-GANISR: gradual generative adversarial network for image super resolution. Neurocomputing. 366, 140\u2013153 (2019)","journal-title":"Neurocomputing."},{"key":"1019_CR23","unstructured":"Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. Eighth IEEE Int. Conf. Comput. Vis. ICCV 2001, Vancouver (2001)"},{"key":"1019_CR24","unstructured":"Kingma, D.P., Adam, J.L.B.: A method for stochastic optimization. http:\/\/arxiv.org\/abs\/1412.6980 (2014)"},{"key":"1019_CR25","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proc. Thirteenth Int. Conf. Artif. Intell. Stat. (AISTATS), Chia Laguna (2010)"},{"key":"1019_CR26","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jozefowicz, R., Jia, Y., Kaiser, L., Kudlur, M., Levenberg, J., Man\u00e9, D., Schuster, M., Monga, R., Moore, S., Murray, D., Olah, C., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Vi\u00e9gas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: large-scale machine learning on heterogeneous systems. In: 12th Symp. Oper. Syst. Des. Implement. (OSDI 16), Savannah (2015)"},{"key":"1019_CR27","unstructured":"Lenc, A., Karel, V.: MatConvNet: convolutional neural networks for MATLAB. In: Proc. 23rd ACM Int. Conf. Multimed., Brisbane (2015)"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-020-01019-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-020-01019-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-020-01019-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T02:04:33Z","timestamp":1632189873000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-020-01019-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,21]]},"references-count":27,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["1019"],"URL":"https:\/\/doi.org\/10.1007\/s11554-020-01019-1","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,21]]},"assertion":[{"value":"4 April 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 September 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}