Hui Li, Xiao-Jun Wu*
SpringerLink: https://link.springer.com/chapter/10.1007/978-3-319-71607-7_59
arXiv: https://arxiv.org/abs/1804.08355
ICIG2017(oral)
Framework
Dictionary learning
Reconstructure
1 made_images and made_images_new are the source images which contain different focus region.
2 image_vector and image_vector_new are the image patches matrices and each column is an imape patch which divided by sliding window technique.
3 dictionary and dictionary_new are the su-dictionaries from image_vector and image_vector_new.
1 Hog.m---extract the HOG features of image patch.
2 The code of LRR
solve_lrr.m
solve_l1l2.m
inexact_alm_lrr_l1l2.m, inexact_alm_lrr_l1.m
exact_alm_lrr_l1l2.m, exact_alm_lrr_l1.m
3 getClassLabel.m ---- set class label for each patch.
4 fusion_dllrr.m ---- main file.
5 The tool boxes of KSVD and OMP
The LRR method is proposed by Guangcan Liu in 2010.
"Liu G, Lin Z, Yu Y. Robust Subspace Segmentation by Low-Rank Representation[C]// International Conference on Machine Learning. DBLP, 2010:663-670."
And we just use this method in our paper without change.
@inproceedings{li2017multi,
title={Multi-focus image fusion using dictionary learning and low-rank representation},
author={Li, Hui and Wu, Xiao-Jun},
booktitle={International Conference on Image and Graphics},
pages={675--686},
year={2017},
organization={Cham, Switzerland: Springer}
}