  Diabetic foot ulcers (DFUs) constitute a serious complication for people with
diabetes. The care of DFU patients can be substantially improved through
self-management, in order to achieve early-diagnosis, ulcer prevention, and
complications management in existing ulcers. In this paper, we investigate two
categories of image-to-image translation techniques (ItITT), which will support
decision making and monitoring of diabetic foot ulcers: noise reduction and
super-resolution. In the former case, we investigated the capabilities on noise
removal, for convolutional neural network stacked-autoencoders (CNN-SAE).
CNN-SAE was tested on RGB images, induced with Gaussian noise. The latter
scenario involves the deployment of four deep learning super-resolution models.
The performance of all models, for both scenarios, was evaluated in terms of
execution time and perceived quality. Results indicate that applied techniques
consist a viable and easy to implement alternative that should be used by any
system designed for DFU monitoring.
