Zero-Shot Multi-Focus Image Fusion

Xingyu Hu, Junjun Jiang, Xianming Liu, Jiayi Ma

Abstract: Multi-focus image fusion (MFIF) is an effective way to eliminate the out-of-focus blur generated in the imaging process. The difficulties in focus level estimation and the lack of real training set for supervised learning make MFIF remain a challenging task after decades of research. According to DIP [1], a neural network can capture the low-level statistics of a single image and can be used as a prior for solving many low-level problems. Based on this idea, we propose a novel architecture named IM-Net comprised of I-Net to model the deep prior of the fused image and M-Net to model the deep prior of the focus map. Without any large scale training set, our method achieves zero-shot learning through the extracted prior information. Experiments on extensively used dataset demonstrate the effectiveness of our approach.