Abstract
- We consider the so-called symmetric structural auto-encoder (SyS-AE) for image reconstruction preserving the perceptual properties to be kept in the decoded images for later visual inspection by experts. The images are obtained from micro-gravity experiments on the International Space Station (ISS). This application requires an encoder of low computational complexity and fast execution time due to the limited hardware and energy resources. The proposed SyS-AE uses a non-linear transfer function on vectorized images followed by a linear down-projection as encoder trained by stochastic gradient descent learning using the structural similarity loss. The decoder can be explicitly calculated as a kind of an inverse of the encoder. We demonstrate the ability of the system for the given problem as well as for illustrating MNIST and Fashion-MNIST data.