How to filter noise from continuous path in image
So entropy filter sounded good, but it didn't really work because the actual signal is also considered high entropy, even after adjusting the window.

What I did was take a derivative of all of the data in the time axis (left to right). Then I capped the maximum value. Then I integrated the derivative.

deriv = data[i, 1:] - data[i, :-1]
deriv[deriv &gt; max_] = max_


The derivitave is $ \frac{dy}{dx }$. In my mind dy is a vertical line segment, dx is a horizontal line segment. Putting them together makes a right triangle, where the function is the hypotenuse is f, the horizontal leg is dx and the vertical leg is dy. Thus $ tan^{-1} {\frac{dy}{dx}} $ is the angle of elevation for f. In my mind, putting a maximum on the derivative is like putting a maximum angle on f. It is like saying, "Cap the angle of elevation for f. If it goes above 150 degrees from the horizon, make it smaller."

It seems to work. It limits the maximum angle of elevation for the signal. You can see the original signal (in red) and the signal after derivative filter (in green).


Then I take original signal minus derivative filter applied to original signal. This leaves me with just the noise band, which I can amplify, blur, and then subtract out.