The fusion of input and guidance images that have a tradeoff in their
information (e.g., hyperspectral and RGB image fusion or pansharpening) can be
interpreted as one general problem. However, previous studies applied a
task-specific handcrafted prior and did not address the problems with a unified
approach. To address this limitation, in this study, we propose a guided deep
decoder network as a general prior. The proposed network is composed of an
encoder-decoder network that exploits multi-scale features of a guidance image
and a deep decoder network that generates an output image. The two networks are
connected by feature refinement units to embed the multi-scale features of the
guidance image into the deep decoder network. The proposed network allows the
network parameters to be optimized in an unsupervised way without training
data. Our results show that the proposed network can achieve state-of-the-art
performance in various image fusion problems.Comment: ECCV 202