Laurent Jacques provided this comment on Hackaday and above is the result in subtracting the first measurement (white image) from all the other measurements.
I believe the single white pixel you get at the top left corner is a reconstruction bias. It should dissapear if you center your measurements. Hadamard patterns form a basis only if the patterns are +-1 and not 0/1 as imposed by the optics. However, one can basically subtract to each measurement one the measurement obtained with a fully white pattern to simulate +-1 patterns.
For information, you may already know it but random patterns can work as well (if they are also centered) but then the reconstruction method must be totally different; it becomes non linear and a single sum doesn’t work. In this case, you need to solve an optimization program whose solution is the image. This is explained is certain text books and papers on compressive sensing.
I believe the single white pixel you get at the top left corner is a reconstruction bias. It should dissapear if you center your measurements. Hadamard patterns form a basis only if the patterns are +-1 and not 0/1 as imposed by the optics. However, one can basically subtract to each measurement one the measurement obtained with a fully white pattern to simulate +-1 patterns.
For information, you may already know it but random patterns can work as well (if they are also centered) but then the reconstruction method must be totally different; it becomes non linear and a single sum doesn’t work. In this case, you need to solve an optimization program whose solution is the image. This is explained is certain text books and papers on compressive sensing.
