Maximum Likelihood Estimation - huge bias on certain values, advice?

First of all I profusely apologise for the lack of suitable ways to express myself, I lack the formal data science background and am trying to learn as I go along, so finding the right terminology is a bit tricky for me..

I'm trying to run MLE across a great number of data sets (basically a matrix of pixels), with the idea to predict the optimum parameter of a known model in each pixel.

According to the literature in my field, MLE should be the best estimator for a data closely related to mine (it has to do with counting statistics, hence Poisson distribution), and this indeed works fine.

Overall, the estimated parameters across the entire matrix should roughly form a normal distribution.The very best I can do is get a histogram which looks like this:

As you see the estimator is hugely biased in favour of some values, and hence the overall image doesn't correspond to reality. It seems to be hitting the lower boundary of the estimated parameter, which I've set to 0.1. The greater counts I have, the less the bias is, but this is the maximum counts I can get.

The loglikelihood for such poisson data I also got from the literature, and I got it to work fine for an established model, but not for the new model I am trying now. The model is overall an exponential decay (e^-x/t) with some additional parameters, I have simplified it as much as possible such that only t is to be estimated. I am minimizing the loglikelihood with Nelder-Mead, as it seemed to yield the best results with the established model.

Could anyone kindly point me to the right direction in terms of any suggestions what to try or even better what I have to read, since there is a lot of information on what MLE is and how it works, but troubleeshooting is tricky to start from scratch .. I've been working on this for months and now I am stuck, so any advice at all will be greatly appreciated.