Image SR-based NLM and DCNN improved IBP with cubic B-spline

References

• Deng LJ, Guo W, Huang TZ. Single image super-resolution by approximate heavyside functions. Int J Computer Math Computer Vision Pattern Recogn. 2018;7:107–123.
   Google Scholar
• Rasti, Demirel H, Anbarjafari G . Image resolution enhancement by using interpolation followed by iterative back projection. In Proc. IEEE Conf. Signal Processing and Communication, 2013, pp. 1–4.
   Google Scholar
• Jeong YI, Jeon J, Paik J. Multi-frame example-based super-resolution using locally directional self-similarity. IEEE Conf Consumer Electron. 2015;61:9–12.
   Web of Science ®Google Scholar
• Liu D, Wang Z, Wen YJ. Robust single image super-resolution via deep networks with sparse prior. IEEE Trans Image Process. 2016;25(7):3194–3207. doi: 10.1109/TIP.2016.2564643
   PubMed Web of Science ®Google Scholar
• Wang L, Wu H, Pan C. Fast image up sampling via the displacement field. IEEE Trans Image Process. 2014;23(12):5123–5135. doi: 10.1109/TIP.2014.2360459
   PubMed Web of Science ®Google Scholar
• Li X, Orchard MT. New edge-directed interpolation. IEEE Trans Image Process. 2001;10:1521–1527. doi: 10.1109/83.951537
   PubMed Web of Science ®Google Scholar
• Hou AH. Cubic splines for image interpolation and digital filtering. IEEE Trans Acoustics, Speech Signal Process. 1978;26(6):508–517. doi: 10.1109/TASSP.1978.1163154
   Google Scholar
• Hayat K. Super-resolution via deep learning. Survey on ArXiv, 2018. pp. 1–33.
   Google Scholar
• Lim B, Son S, Kim H. Enhanced deep residual networks for single image super-resolution. In Proceedings of IEEE Conference on Enhanced Deep Residual CVPR, 2017, pp. 136–144.
   Google Scholar
• Lai WS, Huang JB, Ahuja N. Fast and accurate image super-resolution with deep Laplacian pyramid networks. IEEE Trans Pattern Anal Machine Intell. 2018;41:1–14.
   Web of Science ®Google Scholar
• Wang G, Cong W, Yang Q. Tomographic image reconstruction via machine learning. Google patent, 2017.
   Google Scholar
• Cruz C, Mehta R, Katkovnik V. Single image super-resolution based on Wiener filter in similarity domain. IEEE Trans Image Process. 2018;27(3):1376–1389. doi: 10.1109/TIP.2017.2779265
   PubMed Web of Science ®Google Scholar
• Zhu Z, Guo F, Yu H, et al. Fast single image super-resolution via self-example learning and sparse representation. IEEE Trans Multimedia. 2014;16(8):2178–2190. doi: 10.1109/TMM.2014.2364976
   Web of Science ®Google Scholar
• Sun J, Xu Z, Shum H. Image superresolution using gradient profile prior. In Proceedings of. IEEE Conference on Computer Vision Pattern Recogition, 2008.
   Google Scholar
• Ma C, Yuan C, Yang X, et al. Learning a no-reference quality metric for single-image super-resolution. 2017;158:1–16.
   Google Scholar
• Qi Y, Zhang Y, Zhao T, et al. Single image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction. ISA Trans. 2017;82:84–92.
   Web of Science ®Google Scholar
• Sinha S, Dattagupta R, Mukhopadhyay D. An alternate approach for designing a domain specific image search prototype using histogram. In survey on ArXiv. 2013:1–10.
   Google Scholar
• Huang JB, Singh A, Ahuja N. Single image super-resolution from transformed self-exemplars. IEEE Int Conf Computer Vision. 2015.
   Google Scholar
• Fan C, Wu C, Li G, et al. Projections onto convex sets super-resolution reconstruction based on point spread function estimation of low-resolution remote sensing images. Sensors Smart Sens Agric Land Sys. 2017;17(2).
   Google Scholar
• Jiang J, Ma X, Chen C, et al. Single image super-resolution via locally regularized anchored neighborhood regression and nonlocal means. IEEE Trans Image Process. 2017;19:15–26.
   Google Scholar
• Tomasi C, Manduchi R. Bilateral filtering for gray and color images. IEEE Int Conf Computer Vision. 1998;7: 836–846.
   Google Scholar
• Elad M. On the origin of the bilateral filter and ways to improve it. IEEE Trans Image Process. 2002;11(10):1141–1151. doi: 10.1109/TIP.2002.801126
   PubMed Web of Science ®Google Scholar
• Zeng K, Yu J, Wang R, et al. Coupled deep autoencoder for single image super-resolution. IEEE Trans Cybernetics. 2017;47:27–37. doi: 10.1109/TCYB.2015.2501373
   PubMed Web of Science ®Google Scholar
• Gharbi M, Chen J, Barron JT, et al. Deep bilateral learning for real-time image enhancement. ACM Trans Graph. 2017;36(4):118:1–118:12. doi: 10.1145/3072959.3073592
   Web of Science ®Google Scholar
• Dai Q, Yoo S, Kappeler A. Sparse representation-based multiple frame video super-resolution. IEEE Trans Image Process. 2016;26(2):765–781. doi: 10.1109/TIP.2016.2631339
   PubMed Web of Science ®Google Scholar
• Dong W, Zhang L, Shi G, et al. Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Trans Image Process. 2011;20(7): 1838–1857. doi: 10.1109/TIP.2011.2108306
   PubMed Web of Science ®Google Scholar
• Ledig C, Theis L, Huszár F, Caballero J. Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of IEEE Conference on CPVR 17, 2017.
   Google Scholar
• Zhang K, Tao D, Gao X, et al. Learning multiple linear mappings for efficient single image super-resolution. IEEE Trans Image Process. 2015;24(3):846–861. doi: 10.1109/TIP.2015.2389629
   PubMed Web of Science ®Google Scholar
• Bousmalis K, Silberman N, Dohan D. Generative adversarial networks. Trans Vision and Pattern Recognition. 2017;22:256–375.
   Google Scholar
• Guimin L, Wu Q, Lida Q, et al. Image super-resolution using a dilated convolutional neural network. ISA Trans. 2018;275:1219–1230.
   Google Scholar
• Dong C, Loy C, He K, et al. Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell. 2016;38(2):295–307. doi: 10.1109/TPAMI.2015.2439281
   PubMed Web of Science ®Google Scholar
• Lin KY, Wang G. Hallucinated-IQA: No-reference image quality assessment via adversarial learning. proc. IEEE Conf Computer Vision. 2018:732–741.
   Google Scholar
• Kheradpisheh SR, Ganjtabesh M, Thorpe SJ. STDP-based spiking deep convolutional neural networks for object recognition. Trans Neural Network. 2018;99:56–67. doi: 10.1016/j.neunet.2017.12.005
   PubMed Web of Science ®Google Scholar
• Wang X, Wang H, Yang J, et al. A new method for nonlocal means image denoising using multiple images. PLOS One. 2016;11(7):15–23.
   Web of Science ®Google Scholar
• Grewenig S, Zimmer S, Weickert J. Rotationally invariant similarity measures for nonlocal image denoising. J Visual Commun Image Repres. 2011;22(2):117–130. doi: 10.1016/j.jvcir.2010.11.001
   Web of Science ®Google Scholar
• Bevilacqua M, Roumy A, Guillemot C. Low-complexity single-image super-resolution based on nonnegative neighbor embedding. Proceedings of the 23rd British Machine Vision conference, 2012, pp. 1–135.
   Google Scholar
• Prema Chandran, Bonev B, Lian X, et al. Structured forest for fast edge detection. IEEE Conf on Applications of Computer Vision. 2017;19:73–81.
   Google Scholar
• Chang H, Yeung DY, Xiong Y. Super-resolution through neighbor embedding. IEEE Conf Comput Vis Pattern Recog. 2004;3:1–7.
   Google Scholar
• Timofte R, De Smet V, Van Gool L. A+: Adjusted anchored neighborhood regression for fast super-resolution. Asian Conf. Computer Vision. 2014;3:111–126.
   Google Scholar