文件列表:

Demo.m (289, 2014-05-15)
LoadFilter.m (410, 2014-01-03)
MOGparams.mat (276, 2010-11-11)
comp_upto_shift.m (1106, 2011-04-04)
conjgrad_deconv_g.m (1996, 2010-11-11)
conjgrad_deconv_g_smp_wy.m (1956, 2010-11-11)
conjgrad_deconv_i.m (2065, 2010-11-11)
conjgrad_deconv_i_cycconv.m (1552, 2010-11-11)
convfun.m (210, 2010-11-11)
deconv1.m (1811, 2010-11-11)
deconvL2_w.m (2757, 2010-11-11)
deconvSps.m (1924, 2010-11-11)
deconv_diagfe_filt_sps.m (2812, 2011-04-04)
deconv_diagfe_img_sps.m (2764, 2011-04-04)
deconv_freq_filt_gauss.m (3461, 2011-04-04)
deconv_freq_img_gauss.m (3384, 2011-04-04)
deconv_smp_filt_sps.m (2829, 2011-04-04)
downSmpImC.m (350, 2010-11-11)
fftconvf.m (576, 2010-11-11)
filt_y.m (283, 2010-11-11)
fixsize.m (764, 2010-11-11)
flat3DArray.m (363, 2010-11-11)
flp.m (42, 2010-11-11)
getAutoCor.m (1693, 2010-11-11)
getAutoCorCyc.m (857, 2010-11-11)
getAutoCorFreqDiagCov.m (355, 2010-11-11)
getConvMat.m (1068, 2010-11-11)
getCorAb.m (943, 2010-11-11)
getCorAbDiagCov.m (571, 2010-11-11)
getCorAbFreqDiagCov.m (721, 2010-11-11)
getCorAbFreqDiagCovCyc.m (227, 2010-11-11)
getCory.m (250, 2010-11-11)
getCoryCyc.m (360, 2010-11-11)
goodfactor.m (116, 2010-11-11)
multires_deconv.m (1618, 2010-11-11)
normexp.m (144, 2010-11-11)
read_res.m (8468, 2010-11-11)
resizeKer.m (115, 2010-11-11)
set_sizes.m (217, 2010-11-11)
... ...

This package contains implementations of the MAP_k blind deconvolution algorithms described in the paper "Efficient Marginal Likelihood Optimization in Blind Deconvolution" Levin, Weiss, Durand and Freeman, CVPR2011 Usage of this code is free for research goals only. (c) Anat Levin, anat.levin@weizmann.ac.il, March 2011. ------------------------------------------------------------- Quick start: The package contains 5 test*.m files which run each of the 5 algorithms described in the manuscript, on each of the 32 test images of Levin etal's CVPR2009 paper. There are also 5 deconv* functions which set up a deconvolution problem which matches each algorithm. The best algorithm is free energy with a diagonal covariance approximation, in the filters domain (that is, solving for each derivative independently, without enforcing integrability). So if you are mostly interested in a good blind deconvolution algorithm- start with this one. Also note that the sampling covariance approximation algorithm is *very* slow. data directories: test_data/ - contains the 32 test images resdir/- a subset of the results of the 5 test* scripts, as well as Fergus and Cho algorithms. (We could not include all results due to the limits on the size of the supplementary file.) To read the results and plot error statistics, see the file read_res.m ---------------- More on the structure of the code: The attached code is meant to be a general enough to handle several versions of MAP_k algorithms. The main object passed between the functions and carrying the deconvolution problem is a struct "prob" This struct have fields indicating the various algorithmic options, as well as the imgs and kernel data. See fields description below. We have 5 different deconv* functions which set the problem parameters for each of the 5 implemented algorithms. After setting the relevant parameters the function 'multires_deconv' is called. This function builds a pyramid, downscale and upscale the problem between the different layers. To solve for each layer, it calls deconv1 Deconv1 basically alternate between updating x and updating k, we have several different functions for updating x and k, depending on the specific algorithm chosen. Data fields: prob.y- blurred image prob.k- current kernel estimation prob.filty -derivatives of blurred image when solving in the filter space prob.x- current x estimation prob.filtx- current estimation of x derivatives (when solving in the filter space, that's our actual estimated data) prob.xcov- cell of xcov, (if we solve in the filter space, there is a separate covariance for every filter. When we assume a diagonal covariance or a frequency diagonal covariance, this is a matrix whose size is equal to the size of the image x. When we use the sampling approach we also compute non diagonal elements, so this is a sparse NxN matrix. prob.freeeng- the free energy at every iteration, useful as a sanity check that we are doing things right- the free energy should decrease from iteration to iteration. however, note that since in practice we change the noise variance in each iteration, we ruin the expected decrease. Also, to ensure proper decrease we should use the x from the previous iteration and not initialize in every iter (see prob.init_x_every_itr below) Algorithmic option fields: prob.prior_pi, prob.prior_ivar- MOG prior parameters- weights and inverse variance of each mixture component prob.filts- the filters on which the prior is placed. Our default are the simple derivatives [-1 1], [-1 1]^T, but more sophisticated filters can be used. prob.cycconv- do we assume the convolution is cyclic? in most cases not. (relevant only for the frequency deconvolution case). prob.covtype- the type of covariance matrix approximation- can be 'diag', 'freqdiag' or 'smp' prob.update_x- how do we solve for x- 'conjgrad' solver or 'freqdeconv'; prob.filt_space- do we represent the problem in the filter (derivatives) space or in the image space. that is do we actually solve for the image, or just for an independent set of derivatives, without enforcing integrability. prob.init_x_every_itr- do we initialize x in every update x iteration, and start the iterative reweighted least squares from scratch, or do we use the solution from the previous iteration. We noticed that the convergence is better when we initialize every iteration. However, to ensure that the free energy is perfectly decreasing from iteration to iteration, we actually need to continue from the previous solution. prob.k_prior_ivar-regularization weight when solving for the kernel prob.cycconv- do we assume the convolution is cyclic? in most cases not. (relevant only for the frequency deconvolution case).

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