文件列表:

code\buildDictionary.m (351, 2018-03-05)
code\code.zip (952968, 2016-02-15)
code\dictionary.mat (942035, 2016-02-15)
code\EvaluateInpainting.m (1207, 2016-02-15)
code\get_degrees.m (1021, 2018-03-08)
code\inPainting.m (1120, 2018-03-08)
code\OMP.m (1913, 2018-03-09)
code\overDCTdict.m (415, 2018-03-08)
code\overlap_col2im.m (2328, 2018-03-08)
code\overlap_im2col.m (674, 2016-02-15)
code\peel_mask.m (1389, 2018-03-08)
code\runtime.m (40, 2016-02-15)
code\SMP.m (1697, 2016-02-15)
report.pdf (2923356, 2016-02-15)
code (0, 2018-03-08)

#Image Inpainting Project: ETH Computational Intelligence Lab 2015 This file describes matlab files that we used to obtain our results, together with instructions for how can they be reproduced. I. File List ------------ * buildDictionary.m: Returns a prebuild matrix 'dictionary.mat' or builds a twice overcomplete DCT of supplied dimension if such matrix is not found * dictionary.mat: Our dictionary obtained with K-SVD algorithm * get_degrees: Takes image mask M in 2-D image form and counts for each pixel the number of its neighbours in mask * inPainting.m: Perform the actual inpainting of the image * OMP.m: Our implementation of the Orthogonal Matching Pursuit * overDCTdict.m: Computes overcomplete Discrete Cosine Transform of supplied dimensions * overlap_col2im.m: Recombines overlapping (d x d) patches into an image reconstruction weighting them by their signal-to-noise ration * overlap_im2col.m: Extracts (d x d) patches from image I after every 'overlap' pixels * peel_mask.m: Removes boundary pixel layer from the mask * SMP.m: Performs sequential mask peeling inpainting algorithm, as described in our paper II. Reproducing results ----------------------- Anyone wishing to reproduce our results should use the default parameter values specified in the files. To reconstruct a single channel image, one needs to perform the following. Represent the grayscale image as a matrix I in [0,1] range and get a binary mask matrix of size(I) with zeros at missing pixels. Now execute: 1. I_mask = I; 2. I_mask(mask == 0) = 0; 3. I_rec = inPainting(I_mask, mask); The reconstructed image is single channel a matrix I_rec in [0,1] range of the same size as I. III. Authors ------------ Filippini Luca Teodoro, Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland. E-mail: fteodoro@student.ethz.ch Porvaznik Michal, Department of Computer Science, ETH Zurich, 8092 Zurich, Switzer- land. E-mail: pmichal@student.ethz.ch Trujic Milos, Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland. E-mail: mtrujic@student.ethz.ch

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