Multi-focus Image Fusion using Fully Convolutional Two-stream Network for Visual Sensors

• Kaiping Xu,
• Zheng Qin,
• Guolong Wang,
• Huidi Zhang,
• Kai Huang,
• Shuxiong Ye,

Vol. 12, No. 5, May 30, 2018
10.3837/tiis.2018.05.019, Download Paper (Free):

• Multi-focus image fusion
• fully convolutional neural network
• deconvolution decoder
• convolution fusion

Abstract

We propose a deep learning method for multi-focus image fusion. Unlike most existing pixel-level fusion methods, either in spatial domain or in transform domain, our method directly learns an end-to-end fully convolutional two-stream network. The framework maps a pair of different focus images to a clean version, with a chain of convolutional layers, fusion layer and deconvolutional layers. Our deep fusion model has advantages of efficiency and robustness, yet demonstrates state-of-art fusion quality. We explore different parameter settings to achieve trade-offs between performance and speed. Moreover, the experiment results on our training dataset show that our network can achieve good performance with subjective visual perception and objective assessment metrics.

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