An edge preserving high-order PDE for multiframe image super-resolution
Tài liệu tham khảo
Peleg, 2014, A statistical prediction model based on sparse representations for single image super-resolution, IEEE Trans. Image Process., 23, 2569, 10.1109/TIP.2014.2305844
Cruz, 2018, Single image super-resolution based on wiener filter in similarity domain, IEEE Trans. Image Process., 27, 1376, 10.1109/TIP.2017.2779265
Zhang, 2015, Learning multiple linear mappings for efficient single image super-resolution, IEEE Trans. Image Process., 24, 846, 10.1109/TIP.2015.2389629
Farsiu, 2006, Multiframe demosaicing and super-resolution of color images, IEEE Trans. Image Process., 15, 141, 10.1109/TIP.2005.860336
Maiseli, 2014, A robust super-resolution method with improved high-frequency components estimation and aliasing correction capabilities, J. Frankl. Inst., 351, 513, 10.1016/j.jfranklin.2013.09.007
Laghrib, 2018, Simultaneous deconvolution and denoising using a second order variational approach applied to image super resolution, Comput. Vis. Image Underst., 168, 50, 10.1016/j.cviu.2017.08.007
Robinson, 2010, New applications of super-resolution in medical imaging, Super Resolut. Imaging, 2010, 384
Greenspan, 2008, Super-resolution in medical imaging, Comput. J., 52, 43, 10.1093/comjnl/bxm075
Tatem, 2001, Super-resolution target identification from remotely sensed images using a hopfield neural network, IEEE Trans. Geosci. Remote Sens., 39, 781, 10.1109/36.917895
Park, 2003, Super-resolution image reconstruction: a technical overview, IEEE Signal Process. Mag., 20, 21, 10.1109/MSP.2003.1203207
Werlberger, 2009, Anisotropic Huber-L1 optical flow, 1, 3
Tsai, 1984, Multiframe image restoration and registration
Zhang, 2012, A super-resolution reconstruction algorithm for hyperspectral images, Signal Process., 92, 2082, 10.1016/j.sigpro.2012.01.020
He, 2007, A nonlinear least square technique for simultaneous image registration and super-resolution, IEEE Trans. Image Process., 16, 2830, 10.1109/TIP.2007.908074
Rudin, 1992, Nonlinear total variation based noise removal algorithms, Phys. D Nonlinear Phenom., 60, 259, 10.1016/0167-2789(92)90242-F
Ng, 2007, A total variation regularization based super-resolution reconstruction algorithm for digital video, EURASIP J. Adv. Signal Process., 2007, 10.1155/2007/74585
Farsiu, 2004, Fast and robust multiframe super resolution, IEEE Trans. Image Process., 13, 1327, 10.1109/TIP.2004.834669
Zeng, 2013, A robust multiframe super-resolution algorithm based on half-quadratic estimation with modified BTV regularization, Digit. Signal Process., 23, 98, 10.1016/j.dsp.2012.06.013
Li, 2010, A multi-frame image super-resolution method, Signal Process., 90, 405, 10.1016/j.sigpro.2009.05.028
Laghrib, 2015, A combined total variation and bilateral filter approach for image robust super resolution, EURASIP J. Image Video Process., 2015, 1, 10.1186/s13640-015-0075-4
Marquina, 2008, Image super-resolution by TV-regularization and Bregman iteration, J. Sci. Comput., 37, 367, 10.1007/s10915-008-9214-8
Ren, 2013, Fractional order total variation regularization for image super-resolution, Signal Process., 93, 2408, 10.1016/j.sigpro.2013.02.015
Yuan, 2012, Multiframe super-resolution employing a spatially weighted total variation model, IEEE Trans. Circuits Syst. Video Technol., 22, 379, 10.1109/TCSVT.2011.2163447
Zeng, 2015, Image super-resolution employing a spatial adaptive prior model, Neurocomputing, 162, 218, 10.1016/j.neucom.2015.03.049
Chen, 2012, Video super-resolution using generalized gaussian Markov random fields, IEEE Signal Process. Lett., 19, 63, 10.1109/LSP.2011.2178595
Kanemura, 2009, Superresolution with compound Markov random fields via the variational em algorithm, Neural Netw., 22, 1025, 10.1016/j.neunet.2008.12.005
Rajan, 2002, An MRF-based approach to generation of super-resolution images from blurred observations, J. Math. Imaging Vis., 16, 5, 10.1023/A:1013961817285
Sanguansat, 2012, A robust video super-resolution using a recursive Leclerc Bayesian approach with an OFOM (optical flow observation model), 116
Shechtman, 2005, Space-time super-resolution, IEEE Trans. Pattern Anal. Mach. Intell., 27, 531, 10.1109/TPAMI.2005.85
Song, 2010, An adaptive l 1–l 2 hybrid error model to super-resolution, 2821
Zhang, 2010, A super-resolution reconstruction algorithm for surveillance images, Signal Process., 90, 848, 10.1016/j.sigpro.2009.09.002
Dong, 2011, Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization, IEEE Trans. Image Process., 20, 1838, 10.1109/TIP.2011.2108306
Purkait, 2012, Super resolution image reconstruction through Bregman iteration using morphologic regularization, IEEE Trans. Image Process., 21, 4029, 10.1109/TIP.2012.2201492
Gao, 2015, High performance super-resolution reconstruction of multi-frame degraded images with local weighted anisotropy and successive regularization, Opt. Int. J. Light Electron Opt., 126, 4219, 10.1016/j.ijleo.2015.08.119
Laghrib, 2017, An iterative image super-resolution approach based on Bregman distance, Signal Process. Image Commun., 58, 24, 10.1016/j.image.2017.06.006
Maiseli, 2015, A noise-suppressing and edge-preserving multiframe super-resolution image reconstruction method, Signal Process. Image Commun., 34, 1, 10.1016/j.image.2015.03.001
El Mourabit, 2017, A new denoising model for multi-frame super-resolution image reconstruction, Signal Process., 132, 51, 10.1016/j.sigpro.2016.09.014
Yang, 2010, Image super-resolution via sparse representation, IEEE Trans. Image Process., 19, 2861, 10.1109/TIP.2010.2050625
Mallat, 2010, Super-resolution with sparse mixing estimators, IEEE Trans. Image Process., 19, 2889, 10.1109/TIP.2010.2049927
Gao, 2012, Image super-resolution with sparse neighbor embedding, IEEE Trans. Image Process., 21, 3194, 10.1109/TIP.2012.2190080
Timofte, 2013, Anchored neighborhood regression for fast example-based super-resolution, 1920
Jiang, 2017, Single image super-resolution via locally regularized anchored neighborhood regression and nonlocal means, IEEE Trans. Multimed., 19, 15, 10.1109/TMM.2016.2599145
Deng, 2016, Similarity constraints-based structured output regression machine: an approach to image super-resolution, IEEE Trans. Neural Netw. Learn. Syst., 27, 2472, 10.1109/TNNLS.2015.2468069
Tang, 2018, Combining sparse coding with structured output regression machine for single image super-resolution, Inf. Sci., 430, 577, 10.1016/j.ins.2017.12.001
Dong, 2014, Learning a deep convolutional network for image super-resolution, 184
Dong, 2016, Accelerating the super-resolution convolutional neural network, 391
Dong, 2016, Image super-resolution using deep convolutional networks, IEEE Trans. Pattern Anal. Mach. Intell., 38, 295, 10.1109/TPAMI.2015.2439281
Yue, 2018, An external learning assisted self-examples learning for image super-resolution, Neurocomputing, 312, 107, 10.1016/j.neucom.2018.05.076
Wang, 2015, Deep networks for image super-resolution with sparse prior, 370
Lin, 2018, Image super-resolution using a dilated convolutional neural network, Neurocomputing, 275, 1219, 10.1016/j.neucom.2017.09.062
Chen, 2018, Cisrdcnn: Super-resolution of compressed images using deep convolutional neural networks, Neurocomputing, 285, 204, 10.1016/j.neucom.2018.01.043
Lin, 2018, Deep unsupervised learning for image super-resolution with generative adversarial network, Signal Process. Image Commun., 68, 88, 10.1016/j.image.2018.07.003
You, 2000, Fourth-order partial differential equations for noise removal, IEEE Trans. Image Process., 9, 1723, 10.1109/83.869184
Yi, 2006, Fourth-order partial differential equations for image enhancement, Appl. Math. Comput., 175, 430
Hermosilla, 2008, Non-linear fourth-order image interpolation for subpixel edge detection and localization, Image and Vis. Comput., 26, 1240, 10.1016/j.imavis.2008.02.012
Hajiaboli, 2011, An anisotropic fourth-order diffusion filter for image noise removal, Int. J. Comput. Vis., 92, 177, 10.1007/s11263-010-0330-1
Weickert, 1998, 1
Hundsdorfer, 2013, 33
Calatroni, 2014, ADI splitting schemes for a fourth-order nonlinear partial differential equation from image processing, Discrete Contin. Dyn. Syst. A, 34, 931, 10.3934/dcds.2014.34.931
Perona, 1990, Scale-space and edge detection using anisotropic diffusion, IEEE Trans. Pattern Anal. Mach. Intell., 12, 629, 10.1109/34.56205
You, 1996, Behavioral analysis of anisotropic diffusion in image processing, IEEE Trans. Image Process., 5, 1539, 10.1109/83.541424
Laghrib, 2016, A multi-frame super-resolution using diffusion registration and a nonlocal variational image restoration, Comput. Math. Appl., 72, 2535, 10.1016/j.camwa.2016.09.013
Yuanji, 2003, Image quality evaluation based on image weighted separating block peak signal to noise ratio, 2, 994
Sheikh, 2005, An information fidelity criterion for image quality assessment using natural scene statistics, IEEE Trans. Image Process., 14, 2117, 10.1109/TIP.2005.859389
Wang, 2003, Multiscale structural similarity for image quality assessment, 2, 1398
Nelson, 2012, Performance evaluation of multi-frame super-resolution algorithms, 1
Zhao, 2016, A generalized detail-preserving super-resolution method, Signal Process., 120, 156, 10.1016/j.sigpro.2015.09.006