  Many image restoration techniques are highly dependent on the degradation
used during training, and their performance declines significantly when applied
to slightly different input. Blind and universal techniques attempt to mitigate
this by producing a trained model that can adapt to varying conditions.
However, blind techniques to date require prior knowledge of the degradation
process, and assumptions regarding its parameter-space. In this paper we
present the Self-Normalization Side-Chain (SCNC), a novel approach to blind
universal restoration in which no prior knowledge of the degradation is needed.
This module can be added to any existing CNN topology, and is trained along
with the rest of the network in an end-to-end manner. The imaging parameters
relevant to the task, as well as their dynamics, are deduced from the variety
in the training data. We apply our solution to several image restoration tasks,
and demonstrate that the SNSC encodes the degradation-parameters, improving
restoration performance.
