Time-of-Flight data is typically affected by a high level of noise and by artifacts due to Multi-Path Interference (MPI). While various traditional approaches for ToF data improvement have been proposed, machine learning techniques have seldom been applied to this task, mostly due to the limited availability of real world training data with depth ground truth. In this paper, we avoid to rely on labeled real data in the learning framework. A Coarse-Fine CNN, able to exploit multi-frequency ToF data for MPI correction, is trained on synthetic data with ground truth in a supervised way. In parallel, an adversarial learning strategy, based on the Generative Adversarial Networks (GAN) framework, is used to perform an unsupervised pixel-level domain adaptation from synthetic to real world data, exploiting unlabeled real world acquisitions. Experimental results demonstrate that the proposed approach is able to effectively denoise real world data and to outperform stateof-the-art techniques.

By placing supports at the disposal of the patients, it is possible to improve significantly the compliance after surgery. To succeed in using the prone position also at home it is necessary to supply the patient with a support which is comfortable, cheap and easy to handle.

With the help of sufficient support at the disposal of the patients, it is possible to significantly improve the compliance during the period of "fdp" after surgery. In order to get a safe and painless "fdp" in bed, an ergonomic body positioning is necessary (Schaefer 2012). By practicing the "fdp" until the gas bubble is completely resorbed, the risk of developing a postoperative cataract can be reduced significantly. Provided there is a good compliance to "fdp", the gas bubble can cause the desired tamponade effect even when using shorter acting gases. By performing a consistent "fdp" it is possible to accelerate the healing process and avoid reoperations. Hereby it should even be possible to use an SF6-air mix or optionally simply air as tamponade.

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