文件列表:

elleSR_0.2\elle_eval_huber.cpp (2165, 2012-02-23)
elleSR_0.2\elle_eval_huber.mexw32 (8704, 2012-03-24)
elleSR_0.2\elle_eval_huber_grad.cpp (3214, 2012-02-23)
elleSR_0.2\elle_eval_huber_grad.mexw32 (9216, 2012-03-24)
elleSR_0.2\getAvim.cpp (9529, 2012-02-23)
elleSR_0.2\getAvim.mexw32 (10752, 2012-03-24)
elleSR_0.2\lena.png (151199, 2010-02-23)
elleSR_0.2\makeLR.cpp (8634, 2012-02-23)
elleSR_0.2\makeLR.mexw32 (11776, 2012-03-24)
elleSR_0.2\makeW.cpp (12422, 2012-02-23)
elleSR_0.2\makeW.mexw32 (11776, 2012-03-24)
elleSR_0.2\mex_amub.cpp (5219, 2012-02-23)
elleSR_0.2\mex_amub.mexw32 (9728, 2012-03-24)
elleSR_0.2\netlab3_3\conffig.m (942, 2004-06-18)
elleSR_0.2\netlab3_3\confmat.m (1349, 2004-06-18)
elleSR_0.2\netlab3_3\conjgrad.m (4715, 2004-06-18)
elleSR_0.2\netlab3_3\consist.m (2607, 2004-06-18)
elleSR_0.2\netlab3_3\Contents.m (11172, 2004-06-18)
elleSR_0.2\netlab3_3\convertoldnet.m (724, 2004-06-18)
elleSR_0.2\netlab3_3\datread.m (2611, 2004-06-18)
elleSR_0.2\netlab3_3\datwrite.m (1141, 2004-06-18)
elleSR_0.2\netlab3_3\dem2ddat.m (1362, 2004-06-18)
elleSR_0.2\netlab3_3\demard.m (6175, 2004-06-18)
elleSR_0.2\netlab3_3\demev1.m (5511, 2004-06-18)
elleSR_0.2\netlab3_3\demev2.m (8201, 2004-06-18)
elleSR_0.2\netlab3_3\demev3.m (5718, 2004-06-18)
elleSR_0.2\netlab3_3\demgauss.m (2267, 2004-06-18)
elleSR_0.2\netlab3_3\demglm1.m (2449, 2004-06-18)
elleSR_0.2\netlab3_3\demglm2.m (2552, 2004-06-18)
elleSR_0.2\netlab3_3\demgmm1.m (6067, 2004-06-18)
elleSR_0.2\netlab3_3\demgmm2.m (4593, 2004-06-18)
elleSR_0.2\netlab3_3\demgmm3.m (5799, 2004-06-18)
elleSR_0.2\netlab3_3\demgmm4.m (6414, 2004-06-18)
elleSR_0.2\netlab3_3\demgmm5.m (4886, 2004-06-18)
elleSR_0.2\netlab3_3\demgp.m (4614, 2004-06-18)
elleSR_0.2\netlab3_3\demgpard.m (4167, 2004-06-18)
elleSR_0.2\netlab3_3\demgpot.m (1036, 2004-06-18)
elleSR_0.2\netlab3_3\demgtm1.m (4889, 2004-06-18)
elleSR_0.2\netlab3_3\demgtm2.m (5579, 2004-06-18)
elleSR_0.2\netlab3_3\demhint.m (1509, 2004-06-18)
... ...

Bayesian super-resolution This is Lyndsey's SR code from: http://www.robots.ox.ac.uk/~elle/SRcode/ Start by making sure you have Netlab and mex_amub.cpp, and that all mex files have been compiled for your machine. I also have an nrand.h to define g***ev.c and ran1.c from Numerical Recipes, which are used in generating the iid noise samples in makeLR.c. Running SR_demo for a quick intro to the simple functions. -------------------------------------------------------------------------- A FEW MORE NOTES: The W produced by this code is the TRANSPOSE of the W in my papers -- it's just easier to do it this way in Matlab. That means that W is an N-by-M sparse matrix, where N is the number of pixels in the super-resolution image, and M is the *total* number of low-resolution pixels. It's worth remembering :-) This code works with Matlab structures. The input to the "makeW" function consists of a K-element structure (where there are K low-resolution images), with fields "im" (containing the k^{th} image), H (the 3-by-3 homography mapping pixel locations in the corresponding low-resolution image into the super-resolution frame), la (the multiplicative photometric parameter, lambda_alpha), and lb (the additive photometric parameter, lambda_beta). It is assumed that if H for the k^{th} image is the identity, then its centre lines up with the centre of the super-resolution frame, and there is simply a scaling of "zoom" between them, where the zoom factor is external to the homographies in the struct representation. The Gaussian blur representing the PSF on each low-resolution image has a standard deviation that is measured in *low-resolution pixels* -- note that this differs from a number of super-resolution papers, which use the blur measured on the super-resolution image (which makes less sense!). -------------------------------------------------------------------------- Generating Synthetic Data: IN: o(k).v -- vertical size of LR image o(k).h -- horizontal size of LR image o(k).H -- 3x3 matrix mapping from LR image to common reference plane o(k).la -- photometric prarm (multiplicative) o(k).lb -- photometric prarm (additive) o(k).n -- noise standard deviation o(k).g -- standard deviation of Gaussian PSF (gamma) OUT: o(k).im -- low-resolution image o(k).noise -- noise image o(k).orig -- original (no-noise) image -------------------------------------------------------------------------- Generating the System Matrix, W: IN: o -- includes LR images, PSF param and {geometric, photometric} registration params. OUT: W -- transpose of system matrix Y -- M-by-1 vector of all LR image pixels in column-major order La -- M-by-1 vector of multiplicative photometric factors Lb -- M-by-1 vector of additive photometric terms SR seeks the best x such that Y = diag(La)*W'*x+Lb. -------------------------------------------------------------------------- -------------------------------------------------------------------------- SUMMARY OF FILES: M-files: SR_demo.m [o,gtruth] = synthdata_demo; [im,opts,flog,plog,sclog] = superres_ml(W,Y,La,Lb,avim,opts); im_huber = superres_huber(W,Y,La,Lb,avim,alp,nu,opts); Mex files: makeLR.c (see below for further description) makeW.c (see below for further description) getAvim.c (see below for further description) elle_eval_huber.c eout = elle_eval_huber(x,alp,bet,biv,bih); elle_eval_huber_grad.c g = elle_eval_huber_grad(x,alp,bet,biv,bih); /* [im,noise,orig] = makeLR(biv,bih,o); * * INPUTS: * biv, bih: vertical and horizontal sizes for the high-res image. * o: the super-res problem datastructure, which must be a MATLAB struct with the following entries: * o.H is a 3x3 double representing the homography taking points in the LR image into the SR image. * o.la is the multiplicative photometric parameter. * o.lb is the additive photometric parameter. * o.g is the sigma for the Gaussian PSF. * o.n is the sigma for the additive Gaussian iid noise on each pixel. * o.v vertical size for the image. * o.h horizontal size for the image. * NOTE: if o is larger than 1x1, only the first part is used (i.e. this function doens't * currently produce a stack of images, just one at a time). * * OUTPUTS: * im: the generated low-resolution image, including image noise. * noise: the noise image. * orig: the low-resolution image without the added noise. */ /* [W,Y,La,Lb,M] = makeW(biv,bih,o); * * INPUTS: * biv, bih: vertical and horizontal sizes for the high-res image. * o: the super-res problem datastructure, which must be a MATLAB struct with the following entries: * o(i).H is a 3x3 double representing the homography taking points in the i^th LR image into the SR image. * o(i).la is the multiplicative photometric parameter for image i. * o(i).lb is the additive photometric parameter for image i. * o(i).g is the sigma for the Gaussian PSF associated with image i. * o(i).im is the i^th image. W doesn't depend on image pixels, but the image size is important. * * OUTPUTS: * W: the super-res system matrix. Note that this is the TRANSPOSE of most of my papers' "W" matrices, since it's * rather easier to compute this way. * Y: A KMx1 vector containing all the low-res pixel values, where "KM" is the total number of low-res pixels in the data. * La: a KMx1 vector of multiplicative photometric params (yes, lots of repeated values). * Lb: a KMx1 vector of additive photometric params. * M: a 10xK matrix of geometric registration values. */ /* [avim,msk,M] = getAvim(biv,bih,o); * * INPUTS: * biv, bih: vertical and horizontal sizes for the average image. * o: the super-res problem datastructure, which must be a MATLAB struct with the following entries: * o(i).H is a 3x3 double representing the homography taking points in the i^th LR image into the SR image. * o(i).la is the multiplicative photometric parameter for image i. * o(i).lb is the additive photometric parameter for image i. * o(i).g is the sigma for the Gaussian PSF associated with image i. * o(i).im is the i^th image. W doesn't depend on image pixels, but the image size is important. * * OUTPUTS: * avim: the average image (which will be of size biv-by-bih). * msk: a double matrix of size biv-by-bih indicating which pixels in avim were estimated. * -- average image pixels with negligible support from any of the PSFs of the low-res * image pixels are returned as zeros. * M: a 10xK matrix of geometric registration values. * */

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