I finally could solve my issue by generating a more complex loss function.
By reading again a comment from ptrblck, where he states that I should get the intermediate activation maps and add them to my loss function, I was able to generate plausible results. I’m not sure if it was my misunderstanding or if the related papers are not sufficiently clear regarding this information, but it seems very important that not only you optimize your model for the feature maps of interest. Optimizing the model for the previous feature maps contribute greatly to the final result.
Finally, I would like to state that I have read many tutorials and implementations of perceptual loss in the mean time. The curious thing is that the solution now used by me and previously suggested by ptrblck, is only used for style transfer, at least from what I have read. For super-resolution, the examples I have seen suggests that my first approach (only computing loss using the feature maps of interest) is the correct one, but I highly doubt that. Thanks ptrblck for your support and I hope my experience help others in the future.