  Convolutional Neural Networks (CNNs) are the current de-facto models used for
many imaging tasks due to their high learning capacity as well as their
architectural qualities. The ubiquitous UNet architecture provides an efficient
and multi-scale solution that combines local and global information. Despite
the success of UNet architectures, the use of upsampling layers can cause
artefacts. In this work, a method for assessing the structural biases of UNets
and the effects these have on the outputs is presented, characterising their
impact in the Fourier domain. A new upsampling module is proposed, based on a
novel use of the Guided Image Filter, that provides spectrally consistent
outputs when used in a UNet architecture, forming the Guided UNet (GUNet). The
GUNet architecture is applied and evaluated for example applications of inverse
tone mapping/dynamic range expansion and colourisation from grey-scale images
and is shown to provide higher fidelity outputs.
