  A significant number of researchers have applied deep learning methods to
image fusion. However, most works require a large amount of training data or
depend on pre-trained models or frameworks to capture features from source
images. This is inevitably hampered by a shortage of training data or a
mismatch between the framework and the actual problem. Deep Image Prior (DIP)
has been introduced to exploit convolutional neural networks' ability to
synthesize the 'prior' in the input image. However, the original design of DIP
is hard to be generalized to multi-image processing problems, particularly for
image fusion. Therefore, we propose a new image fusion technique that extends
DIP to fusion tasks formulated as inverse problems. Additionally, we apply a
multi-channel approach to enhance DIP's effect further. The evaluation is
conducted with several commonly used image fusion assessment metrics. The
results are compared with state-of-the-art image fusion methods. Our method
outperforms these 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.
