One of the motivations to use the $ {L}_{2} $ norm comes from the Maximum a Posteriori Estimation (MAP) framework.
If you model $ \psi \left( u \right) \sim \mathcal{N} \left( 0, \alpha \right) $ then if you derive the MAP Estimator in case the added noise is Guassian you'd get the exact model you posted above.