I have an imaging problem. I essentially have a metallic plate with a number of holes through it. I am looking at the number of holes with a thermal camera. While looking at the plate I have two phases of this analysis, 1.a source of hot fluid is flowing through the holes and behind the plate 2.a source of cold fluid flowing behind the plate and through the holes.
During each phase I take a number of images N. So in effect I have a set of random variables for each pixel in the image. $H[x,y] = (x_1, x_2 .... x_n)$ and similarly $C[x,y]=(x_1, x_2 .... x_n)$ where H is the hot frames, C is the cold frames , x-y are the focal plane position of that particular pixel and 1,2,3 are the discrete time of the reading x.
As it stands I'm just taking the mean value for H and C then subtracting them to generate an image ($I=\bar{H} - \bar{C}$ where the bar denotes the expected value) to gain an image that has significantly decreased noise and removes much of the background effects.
This is really just to decrease the noise but I think there is more information there that I'm not utilizing considering this is not a wide sense stationary process, but is evolving with time as the temperatures reach equilibrium for each phase.
I'm looking for a way to utilize this data and for any recommendations on processing this time evolution to form an image. Any suggestions?
I have tried Finite Difference Operator to approximate first and second derivatives, but this and various other algorithms have failed me.