TY  - INPR
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
JF  - IEEE Transactions on Multimedia
PB  - Institute of Electrical and Electronics Engineers (IEEE)
A1  - Zhang, Juncheng
A1  - Liao, Qingmin
A1  - Ma, Haoyu
A1  - Xue, Jing-Hao
A1  - Yang, Wenming
A1  - Liu, Shaojun
KW  - Deep learning

KW  - 
Transforms

KW  - 
Feature extraction

KW  - 
Image fusion

KW  - 
Image segmentation

KW  - 
Fuses

KW  - 
Visualization
Y1  - 2024/01/08/
TI  - Exploit the Best of Both End-to-End and Map-Based Methods for Multi-Focus Image Fusion
AV  - public
N2  - Multi-focus image fusion is a technique to fuse the images focused on different depth ranges to generate an all-in-focus image. Existing deep learning approaches to multi-focus image fusion can be categorized as end-to-end methods and decision map based methods. End-to-end methods can generate natural fusion near the focus-defocus boundaries (FDB), but the output is often inconsistent with the input in the areas far from the boundaries (FFB). On the contrary, decision map based methods can preserve original images in the FFB areas, but often generate artifacts near the FDB. In this paper, we propose a dual-branch network for multi-focus image fusion (DB-MFIF) to exploit the best of both worlds, achieving better results in both FDB and FFB areas, i.e. with naturally sharper FDB areas and more consistent FFB areas with the inputs. In our DB-MFIF, an end-to-end branch and a decision map based branch are proposed to mutually assist each other. In addition, to this end, two map-based loss functions are also proposed. Experiments show that our method surpasses existing algorithms on multiple datasets, both qualitatively and quantitatively, and achieves the state-of-the-art performance. The code and model is available on GitHub: https://github.com/Zancelot/DB-MFIF .
ID  - discovery10185309
UR  - http://dx.doi.org/10.1109/tmm.2024.3350924
ER  -