  Images recorded during the lifetime of computer vision based systems undergo
a wide range of illumination and environmental conditions affecting the
reliability of previously trained machine learning models. Image normalization
is hence a valuable preprocessing component to enhance the models' robustness.
To this end, we introduce a new strategy for the cost function formulation of
encoder-decoder networks to average out all the unimportant information in the
input images (e.g. environmental features and illumination changes) to focus on
the reconstruction of the salient features (e.g. class instances). Our method
exploits the availability of identical sceneries under different illumination
and environmental conditions for which we formulate a partially impossible
reconstruction target: the input image will not convey enough information to
reconstruct the target in its entirety. Its applicability is assessed on three
publicly available datasets. We combine the triplet loss as a regularizer in
the latent space representation and a nearest neighbour search to improve the
generalization to unseen illuminations and class instances. The importance of
the aforementioned post-processing is highlighted on an automotive application.
To this end, we release a synthetic dataset of sceneries from three different
passenger compartments where each scenery is rendered under ten different
illumination and environmental conditions: see https://sviro.kl.dfki.de
