Why do we assume zero mean noise in sensor data? I am reading a paper on measuring respiratory patterns from video data. In defining the model, the authors formulate the problem mathematically as:


  $x_i(t)=h_i(t) \ast g(t) + n_i(t) $


Where $n_i(t)$ is a zero mean noise. My question is why do we model the pixel data as having zero mean noise? What prevents it from having a mean?
The brutally honest answer here is: The noise is considered zero-mean because that's what the author decided to do. Without looking deeper into the signal model employed, it's impossible to answer. 

However, for many systems this makes a lot of sense physically, since the processes leading to a noise realization are very often zero-mean in nature. For example, the thermal noise over any resistor should be zero-mean, simply because, well, that's how these random fluctuations work.