In publications honesty is the best policy.
It's all right to remove outliers and to report descriptive statistics based on the reduced dataset. However in order for others to understand and appreciate what was done you have to state which samples were removed as outliers and your reasoning for why they were outliers.
If your justification of marking those samples as outliers is solid (for example - instrument malfunctioned when they were recorded) then others reading your paper will agree with you and thank you for removing those samples.
If there might be disputes about those samples being outliers then there is always a possibility of doing the calculations twice: one time with outliers present and another time with outliers removed. Then you can present numbers without outliers in the main text and include the numbers calculated with outliers in the supplement.
However it's hard to ignore one part of your question. You write:
removed three outliers ... to improve the models
Here you have to be extra careful. If a sample disagrees with your model it doesn't mean that the sample is outlier. The situation is like this:
- there is a real phenomenon
- you have data about that phenomenon
- you have some imaginary model that you think explains the phenomenon
Now you find that some samples from the real world don't agree with your imaginary model. Would you say that removing those samples will improve your model? Likely not. What might be happening is your model not being a good representation of the real phenomenon. Then by removing these "outliers" you might be ignoring a part of the real world. And in that case removing them is dangerous.
Here is a nice illustration