This program uses a combination of lowpass and highpass filters to remove horizontal or vertical stripes in a cube. The steps behind the noise removal technique are:

       output = input - noise
       where noise = highpass(lowpass(input))
    

Suppose an input image had vertical striping noise, like in the example below. The noise can be removed or suppressed by applying a sequence of lowpass and highpass filters. The idea is to create an image containing only the noise and subtract that noise image from the original.

First, we apply a tall (1xNL) lowpass filter to the input image. This produces an image that is essentially a column average. The high frequency information has been suppressed and the vertical noise is left behind. Note however, the average of the lowpass filter matches the original input so we can not subtract the noise quite yet.

Second, we appy a (NSx1) highpass filter to the lowpass image. This effectively suppress the albedo information in the noise leaving only the noise behind. Note that the noise (third) histogram is centered about zero.

Finally, the noise image is subtracted from the original input image resulting in the desired noise-free image. All the steps the program goes through (along with their respective histograms) are illustrated below:

From left to right: Input, Lowpass, Highpass, Output

This example shows the results of removing vertical striping noise from an image.