A new denoising model for multi-frame super-resolution image reconstruction

[1]  F. Browder.  Nonlinear functional analysis , 1970 .

[2]  Thomas S. Huang,et al.  Multiframe image restoration and registration , 1984 .

[3]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[5]  Edward R. Dougherty,et al.  Gray-scale granulometries compatible with spatial scalings , 1993, Signal Process..

[6]  J. Weickert.  Scale-Space Properties of Nonlinear Diffusion Filtering with a Diffusion Tensor , 1994 .

[7]  Wenyuan Xu,et al.  Behavioral analysis of anisotropic diffusion in image processing , 1996, IEEE Trans. Image Process..

[8]  Joachim Weickert,et al.  Anisotropic diffusion in image processing , 1996 .

[9]  Robert L. Stevenson,et al.  Super-resolution from image sequences-a review , 1998, 1998 Midwest Symposium on Circuits and Systems (Cat. No. 98CB36268).

[10]  Max A. Viergever,et al.  Efficient and reliable schemes for nonlinear diffusion filtering , 1998, IEEE Trans. Image Process..

[11]  Joachim Weickert,et al.  Coherence-enhancing diffusion of colour images , 1999, Image Vis. Comput..

[12]  Peyman Milanfar,et al.  A computationally efficient superresolution image reconstruction algorithm , 2001, IEEE Trans. Image Process..

[13]  Hugh G. Lewis,et al.  Super-resolution target identification from remotely sensed images using a Hopfield neural network , 2001, IEEE Trans. Geosci. Remote. Sens..

[14]  A. Tatema,et al.  Super-resolution land cover pattern prediction using a Hopfield neural network , 2001 .

[15]  Michael Elad,et al.  A fast super-resolution reconstruction algorithm for pure translational motion and common space-invariant blur , 2001, IEEE Trans. Image Process..

[16]  Pierre Kornprobst,et al.  Mathematical problems in image processing - partial differential equations and the calculus of variations , 2010, Applied mathematical sciences.

[17]  Moon Gi Kang,et al.  Super-resolution image reconstruction , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).

[18]  Moon Gi Kang,et al.  Super-resolution image reconstruction: a technical overview , 2003, IEEE Signal Process. Mag..

[19]  Andrew Zisserman,et al.  Computer vision applied to super resolution , 2003, IEEE Signal Process. Mag..

[20]  Joachim Weickert,et al.  A Theoretical Framework for Convex Regularizers in PDE-Based Computation of Image Motion , 2001, International Journal of Computer Vision.

[21]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[22]  Michael Elad,et al.  Advances and challenges in super‐resolution , 2004, Int. J. Imaging Syst. Technol..

[23]  V. Chandran,et al.  Investigation into Optical Flow Super-Resolution for Surveillance Applications , 2005 .

[24]  S. Jitapunkul,et al.  A Robust Iterative Multiframe Super-Resolution Reconstruction using a Huber Bayesian Approach with Huber-Tikhonov Regularization , 2006, 2006 International Symposium on Intelligent Signal Processing and Communications.

[25]  Addisson Salazar,et al.  Optimum Detection of Ultrasonic Echoes Applied to the Analysis of the First Layer of a Restored Dome , 2007, EURASIP J. Adv. Signal Process..

[26]  Somchai Jitapunkul,et al.  A Lorentzian Stochastic Estimation for a Robust Iterative Multiframe Super-Resolution Reconstruction with Lorentzian-Tikhonov Regularization , 2007, EURASIP J. Adv. Signal Process..

[27]  Panos Papamichalis,et al.  Robust Color Image Superresolution: An Adaptive M-Estimation Framework , 2008, EURASIP J. Image Video Process..

[28]  Stanley Osher,et al.  Image Super-Resolution by TV-Regularization and Bregman Iteration , 2008, J. Sci. Comput..

[29]  Tuan Q. Pham,et al.  Robust super-resolution by minimizing a Gaussian-weighted L2 error norm , 2008 .

[30]  Aggelos K. Katsaggelos,et al.  Parameter Estimation in TV Image Restoration Using Variational Distribution Approximation , 2008, IEEE Transactions on Image Processing.

[31]  R. Molina,et al.  Bayesian Super-Resolution image reconstruction using an ℓ1 prior , 2009, 2009 Proceedings of 6th International Symposium on Image and Signal Processing and Analysis.

[32]  Vassilis Anastassopoulos,et al.  Regularized super-resolution image reconstruction employing robust error norms , 2009 .

[33]  Michael Elad,et al.  Generalizing the Nonlocal-Means to Super-Resolution Reconstruction , 2009, IEEE Transactions on Image Processing.

[34]  Daniel Cremers,et al.  Anisotropic Huber-L1 Optical Flow , 2009, BMVC.

[35]  H. Bischof,et al.  2 Anisotropic Huber-L 1 Optical Flow , 2009 .

[36]  Alan C. Bovik,et al.  Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures , 2009, IEEE Signal Processing Magazine.

[37]  Thomas S. Huang,et al.  Image Super-Resolution: Historical Overview and Future Challenges , 2017 .

[38]  Athanasios Voulodimos,et al.  An industrial video surveillance system for quality assurance of a manufactory assembly , 2010, PETRA '10.

[39]  Xuelong Li,et al.  A multi-frame image super-resolution method , 2010, Signal Process..

[40]  Liangpei Zhang,et al.  A super-resolution reconstruction algorithm for surveillance images , 2010, Signal Process..

[41]  H. Brezis.  Functional Analysis, Sobolev Spaces and Partial Differential Equations , 2010 .

[42]  Aggelos K. Katsaggelos,et al.  Variational Bayesian Super Resolution , 2011, IEEE Transactions on Image Processing.

[43]  Toshihisa Tanaka,et al.  Region-based weighted-norm with adaptive regularization for resolution enhancement , 2011, Digit. Signal Process..

[44]  F. Stanco,et al.  Digital Imaging for Cultural Heritage Preservation: Analysis, Restoration, and Reconstruction of Ancient Artworks , 2011 .

[45]  Athanasios Voulodimos,et al.  Bayesian filter based behavior recognition in workflows allowing for user feedback , 2012, Comput. Vis. Image Underst..

[46]  Lihua Yang,et al.  A robust multiframe super-resolution algorithm based on half-quadratic estimation with modified BTV regularization , 2013, Digit. Signal Process..

[47]  Said Raghay,et al.  Robust super resolution of images with non-parametric deformations using an elastic registration , 2014 .

[48]  Giorgio C. Buttazzo,et al.  Variational Analysis in Sobolev and BV Spaces - Applications to PDEs and Optimization, Second Edition , 2014, MPS-SIAM series on optimization.

[49]  Gouda I. Salama,et al.  Adaptive regularization-based super resolution reconstruction technique for multi-focus low-resolution images , 2014, Signal Process..

[50]  Liangpei Zhang,et al.  A locally adaptive L1-L2 norm for multi-frame super-resolution of images with mixed noise and outliers , 2014, Signal Process..

[51]  Nikolaos Doulamis,et al.  5D Modelling: An Efficient Approach for Creating Spatiotemporal Predictive 3D Maps of Large-Scale Cultural Resources , 2015 .

[52]  Huijun Gao,et al.  A noise-suppressing and edge-preserving multiframe super-resolution image reconstruction method , 2015, Signal Process. Image Commun..

[53]  Nikolaos Doulamis,et al.  Deep supervised learning for hyperspectral data classification through convolutional neural networks , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[54]  Said Raghay,et al.  A combined total variation and bilateral filter approach for image robust super resolution , 2015, EURASIP J. Image Video Process..

[55]  Xavier Bresson,et al.  An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization , 2015, NeuroImage.

[56]  Mudar Sarem,et al.  A Generalized Detail-Preserving Super-Resolution method , 2016, Signal Process..

[57]  Jie Li,et al.  Image super-resolution: The techniques, applications, and future , 2016, Signal Process..

[58]  Hyunjoong Kim,et al.  Functional Analysis I , 2017 .