Unsupervised Image Fusion Using Deep Image Priors

• URL: http://arxiv.org/abs/2110.09490v1
• Date: Mon, 18 Oct 2021 17:38:35 GMT
• Title: Unsupervised Image Fusion Using Deep Image Priors
• Authors: Xudong Ma, Alin Achim, Paul Hill
• Abstract summary: Deep Image Prior (DIP) method made it possible to do image restoration totally training-data-free. This paper introduces a novel loss calculation structure, in the framework of DIP, while formulating image fusion as an inverse problem.
• Score: 7.549952136964352
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A significant number of researchers have recently applied deep learning methods to image fusion. However, most of these works either require a large amount of training data or depend on pre-trained models or frameworks. This inevitably encounters a shortage of training data or a mismatch between the framework and the actual problem. Recently, the publication of Deep Image Prior (DIP) method made it possible to do image restoration totally training-data-free. However, the original design of DIP is hard to be generalized to multi-image processing problems. This paper introduces a novel loss calculation structure, in the framework of DIP, while formulating image fusion as an inverse problem. This enables the extension of DIP to general multisensor/multifocus image fusion problems. Secondly, we propose a multi-channel approach to improve the effect of DIP. Finally, an evaluation is conducted using several commonly used image fusion assessment metrics. The results are compared with state-of-the-art traditional and deep learning image fusion methods. Our method outperforms previous techniques for a range of metrics. In particular, it is shown to provide the best objective results for most metrics when applied to medical images.

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