Spectrally Consistent UNet for High Fidelity Image Transformations
- URL: http://arxiv.org/abs/2004.10696v2
- Date: Tue, 29 Sep 2020 09:32:09 GMT
- Title: Spectrally Consistent UNet for High Fidelity Image Transformations
- Authors: Demetris Marnerides, Thomas Bashford-Rogers and Kurt Debattista
- Abstract summary: Convolutional Neural Networks (CNNs) are the current de-facto models used for many imaging tasks.
In this work, a method for assessing the structural biases of UNets and the effects these have on the outputs is presented.
A new upsampling module is proposed, based on a novel use of the Guided Image Filter, that provides spectrally consistent outputs.
- Score: 5.494315657902533
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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