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It is a challenging task to extract the best of both worlds by combining thespatial characteristics of a visible image and the spectral content of aninfrared image. In this work, we propose a spatially constrained adversarialautoencoder that extracts deep features from the infrared and visible images toobtain a more exhaustive and global representation. In this paper, we propose aresidual autoencoder architecture, regularised by a residual adversarialnetwork, to generate a more realistic fused image. The residual module servesas primary building for the encoder, decoder and adversarial network, as an addon the symmetric skip connections perform the functionality of embedding thespatial characteristics directly from the initial layers of encoder structureto the decoder part of the network. The spectral information in the infraredimage is incorporated by adding the feature maps over several layers in theencoder part of the fusion structure, which makes inference on both the visualand infrared images separately. In order to efficiently optimize the parametersof the network, we propose an adversarial regulariser network which wouldperform supervised learning on the fused image and the original visual image.

Image fusion using symmetric skip autoencodervia an Adversarial Regulariser