superresolution_v_2.0.zip图像超分辨率MATLAB实现

SUPERRESOLUTION - v2.0 v1.0 - January 12, 2006 by Patrick Vandewalle, Patrick Zbinden and Cecilia Perez v2.0 - November 13, 2006 by Patrick Vandewalle, Karim Krichane, Cecilia Perez, and Patrick Zbinden Matlab Graphical User Interface for Super-Resolution Imaging To run the application, run "superresolution" in Matlab (with the 'application' directory as working directory) This program allows a user to perform registration of a set of low resolution input images and reconstruct a high resolution image from them. Multiple image registration and reconstruction methods have been implemented. As input, the user can either select existing images, or generate a set of simulated low resolution images from a high resolution image. More information is available online: http://lcavwww.epfl.ch/software/superresolution If you use this software for your research, please also put a reference to the related paper "A Frequency Domain Approach to Registration of Aliased Images with Application to Super-Resolution" Patrick Vandewalle, Sabine Susstrunk and Martin Vetterli available at http://lcavwww.epfl.ch/reproducible_research/VandewalleSV05/ Currently the following algorithms are implemented: Image Registration - D. Keren, S. Peleg, and R. Brada, Image sequence enhancement using sub-pixel displacement, in Proceedings IEEE Conference on Computer Vision and Pattern Recognition, June 1988, pp. 742-746. - L. Lucchese and G. M. Cortelazzo, A noise-robust frequency domain technique for estimating planar roto-translations, IEEE Transactions on Signal Processing, vol. 48, no. 6, pp. 1769-1786, June 2000. - B. Marcel, M. Briot, and R. Murrieta, Calcul de Translation et Rotation par la Transformation de Fourier, Traitement du Signal, vol. 14, no. 2, pp. 135-149, 1997. - P. Vandewalle, S. Susstrunk and M. Vetterli, A Frequency Domain Approach to Registration of Aliased Images with Application to Super-Resolution, accepted to EURASIP Journal on Applied Signal Processing (special issue on Super-resolution), 2005. Image Reconstruction - Interpolation: bicubic interpolation using Matlab's built-in griddata function. - Papoulis-Gerchberg: Papoulis and Gerchberg's POCS algorithm projecting successively onto the space of known pixels and the space of bandlimited images. - Iterated Back Projection: an iterative back projection algorithm inspired by M. Irani and S. Peleg, Improving resolution by image registration, Graphical Models and Image Processing, 53:231-239, 1991. - Robust Super Resolution: a more robust version of the iterative back- projection algorithm using a median instead of a mean, based on A. Zomet, A. Rav-Acha, and S. Peleg, Robust Super-Resolution, Proceedings international conference on computer vision and pattern recognition (CVPR), 2001. - POCS: a projection onto convex sets (POCS) algorithm - Structure-Adaptive Normalized Convolution: algorithm implementing the method from Tuan Q. Pham, Lucas J. van Vliet and Klamer Schutte, Robust Fusion of Irregularly Sampled Data Using Adaptive Normalized Convolution, EURASIP Journal on Applied Signal Processing, Vol. 2006, Article ID 83268, 12 pages, 2006. ----------------------------------------------------------------------- Copyright (C) 2005-2007 Laboratory of Audiovisual Communications (LCAV), Ecole Polytechnique Federale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This software is distributed in the hope that it will be useful, but without any warranty; without even the implied warranty of merchantability or fitness for a particular purpose. See the GNU General Public License for more details (enclosed in the file GPL).