@@ -279,7 +279,7 @@ Calculating the inverse beforehand and using that in the system had marginal tem

\verb|numpy.linalg.solve| runs in $O(N^3)$ for an $N\times N$ matrix, while the multiplication runs in a similar time.~\cite{LAPACKAlgorithms}

The least squares method mentioned in this project also has a shortcoming, but this one cannot be solved as easily.

The pseudoinverse can be calculated beforehand, but the largest problem is that it is solving the system for every pixel individually and calculating the norm.

\verb|numpy.linalg.lstsq| itself runs in $O(N^3)$ for an $N\times N$ matrix~\cite{LeastSquaredProblem}, while the pseudoinverse, when implemented, uses more python runtime, adding to temporal complexity.

\verb|numpy.linalg.lstsq| itself runs in $O(N^3)$ for an $N\times N$ matrix~\cite{LeastSquaredProblem}, while the pseudoinverse, when directly implemented, uses more python runtime, adding to temporal complexity.

This compression suffers when it is only used on individual images, which is not a problem for the use cases of this project.

The test images came from a camera that has 16 image sensors that work simultaneously.