We recently performed a work on signal filtering/component separation (sparse signal/trend/noise). The cost function contains:
- A quadratic data fidelity term,
- Some smoothed $\ell_1$ terms for sparsity and positivity promotion.
This was initially designed for Gaussian noise, and performed satisfactorily. Reviewers asked to test the algorithm on the same signals with Poisson noise, and it performed well also.
In many works concerned with Poisson-Gaussian noise mixtures, authors usually use more involved penalty functions and/or use variance stabilization transforms (such as Anscombe's).
Presenting the work to different signal processing folks, several colleagues (signal and image persons equally) said they were "not surprised" that a simple quadratic term would work well with Poisson noise too.
Since I am less a theoretician than a frequentist, and a bayesian (much) less than a frequentist, I do not understand the reason behind the relatively good behavior with both Gaussian and Poisson perturbations.
May a reader offer practical or theorical hints behind this "non surprise"?