wait, why can it see color? if this algorithm is based on the speed of light traveling (similar to ultrasonic), I would think it won't show color? the resulting image shows the printed 1,2,3 on that paper.
is that because light reflects differently across the same paper due to different colors (black vs. white)?
It uses the time of arrival to determine where a particular reflection came from, but it can also observe the intensity of that reflection to determine reflectivity.
You could easily generate a colour image by using a red, green and blue laser sequentially. But... they haven't done that. It's all black and white so I don't know why you think it can see colour?
I think the mouseover text on the color picture is a mistake. The article labels the blurry grayscale image as a reconstruction and the color image as a regular image.
The two panels in the README are a single image, which is why they share the mouseover text.
Not color, but reflectivity.
super cool!
why julia?
From my experience it is one of the most productive programming languages to develop in, and the code can be iterated on to reach performance that is as good as C or Fortran code.
Note that @krrutkow is the author so this answer is fairly definitive.
I cannot answer for the author, but for projects which build a lot of expensive numerical routines from scratch, Julia is a good fit: The JIT overhead is a small price compared to the high-level feel of the code and the resulting post-compiled speed.
This seems to be one of those “poster child” projects for Julia. As in there are likely lots of custom primitives or numerical routine that’d be impossible to have perform well in Python/Numpy but still saving development effort over coding a custom C++ case (much less C++ expertise needed to do so).