Awesome
MDLatLRR: A novel decomposition method for infrared and visible image fusion
Hui Li, Xiao-Jun Wu*, Josef Kittler
IEEE Trans. Image Process., 2020, doi: 10.1109/TIP.2020.2975984
Note
In 'main.m' file, you will find how to run these codes.
In 'analysis' file, you will find the codes of evaluate metrics.
Platform
MATLAB R2017b on 2.8 GHz Intel(R) Core(TM) i5-8400 CPU with 16 GB RAM.
Fusion framework
Latent Low-Rank Representation
<img src="https://github.com/hli1221/imagefusion_mdlatlrr/blob/master/figures/latentlrr.png" width="600">Multi-level decomposition with Latent LRR
DLatLRR and MDLatLRR
<img src="https://github.com/hli1221/imagefusion_mdlatlrr/blob/master/figures/DLatLRR.png" width="600">MDLatLRR
<img src="https://github.com/hli1221/imagefusion_mdlatlrr/blob/master/figures/MDLatLRR.png" width="600">MDLatLRR for RGBT visual object tracking
The VOT-RGBT2019 sub-challenge benchmark is available at here.
The frames fused by MDLatLRR are fed into two trackers (LADCF, GFSDCF).
The frames in first row and second row are selected from 'car10' and 'car41' (VOT-RGBT 2019), respectively.
First three columns are the results of LADCF. And the last three columns are the tracking results of GFSDCF.
The [RGB] and [infrared] denote the input of trackers is just one modality data (RGB or infrared). The [level-1] to [level-4] demonstrate that the input of trackers is the fused frames which are generated by MDLatLRR.
If you have any question about this code, feel free to reach me(hui_li_jnu@163.com)
Citation
@article{li2020mdlatlrr,
author = {Li, Hui and Wu, Xiao-Jun and Kittler, Josef},
title = {MDLatLRR: A novel decomposition method for infrared and visible image fusion},
note = {doi: 10.1109/TIP.2020.2975984},
year = {2020},
journal = {IEEE Transactions on Image Processing},
publisher={IEEE}
}