Extracting Randomness from a Poisson Distribution
The question's method is not secure absent a model of the source. In particular, applied to a source evolving in time as this sawtoothassumed of period several times the sampling time between Brightness(0) and Brightness(1), the output will be terribly biased towards 0, because the function is decreasing most of the time (this effect will decrease with added Gaussian noise, spacing the samplings).

Another issue is that multiple pixels can output correlated bits.

Both effects might well occur in practice, for a variety of reasons: switching regulator of the power supply of the gizmo, or of the LED source of the ambient lighting. In a cryptographic context, adversaries often have some level of access to a RNG, and might induce that effect on purpose (e.g. varying the power supply, or a light source). And by a corollary of Murphy's law often verified in this very context, such things will happen without explicit adversaries (e.g. as the effect of aging of power supply capacitors) and at the worst time.


  The data generated using this method passed 15/15 NIST tests


If a source fails any statistical test¹, this conclusively proves that the source is bad from a cryptographic perspective. But if the source passes all tests, it can still be horribly bad from a cryptographic perspective; the best that can be reasonably be said is that the source, as tested, was free from the particular defects that the tests are designed to detect.

My though experiment with the sawtooth shows that the source could fail under different, realistic conditions. And it is trivial to design a bit source that passes all tests, yet is terribly weak cryptographically.



¹ significantly more often that predicted by the test's $p$-value, with the test correctly designed, implemented and applied.