lighting-zhu/imagefusion_mdlatlrr

MDLatLRR (IEEE TIP 2020)

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

IEEEXplore

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

Multi-level decomposition with Latent LRR

DLatLRR and MDLatLRR

MDLatLRR

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([email protected])

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}
}