Open Access

Stephan Brehm

• Image manipulation is of historic importance. Ever since the advent of photography, pictures have been manipulated for various reasons. Historic rulers often used image manipulation techniques for the purpose of self-portrayal or propaganda. In many cases, the goal is to manipulate human behaviour by spreading credible misinformation. Photographs, by their nature, portray the real world and as such are more credible to humans. However, image manipulation may not only serve evil purposes. In this thesis, we propose and analyse methods for image manipulation that serve a positive purpose. Specifically, we treat image manipulation as a tool for solving other tasks. For this, we model image manipulation as an image-to-image translation (I2I) task, i.e., a system that receives an image as input and outputs a manipulated version of the input. We propose multiple I2I based methods. We demonstrate that I2I based image manipulation methods can be used to reduce motion blur in videos. Second, weImage manipulation is of historic importance. Ever since the advent of photography, pictures have been manipulated for various reasons. Historic rulers often used image manipulation techniques for the purpose of self-portrayal or propaganda. In many cases, the goal is to manipulate human behaviour by spreading credible misinformation. Photographs, by their nature, portray the real world and as such are more credible to humans. However, image manipulation may not only serve evil purposes. In this thesis, we propose and analyse methods for image manipulation that serve a positive purpose. Specifically, we treat image manipulation as a tool for solving other tasks. For this, we model image manipulation as an image-to-image translation (I2I) task, i.e., a system that receives an image as input and outputs a manipulated version of the input. We propose multiple I2I based methods. We demonstrate that I2I based image manipulation methods can be used to reduce motion blur in videos. Second, we show that I2I based image manipulation methods can be used for domain adaptation and domain extension. Specifically, we present a method that significantly improves the learning of semantic segmentation from synthetic source data. The same technique can be applied to learning nighttime semantic segmentation from daylight images. Next, we show that I2I can be used to enable weakly supervised object segmentation. We show that each individual task requires and allows for different levels of supervision during the training of deep models in order to achieve best performance. We discuss the importance of maintaining control over the output of such methods and show that, with reduced levels of supervision, methods for maintaining stability during training and for establishing control over the output of a system become increasingly important. We propose multiple methods that solve the issues that arise in such systems. Finally, we demonstrate that our proposed mechanisms for control can be adapted to synthesise images from scratch.…