Can Strides Enhance the Efficiency of Moving Average Filters in Image Processing?

Using strides for an efficient moving average filter

Recently, stride-based approaches have gained attention for their efficiency in implementing moving average filters. In this context, we explore how strides can be utilized to enhance the performance of such filters, moving beyond the more traditional convolution-based methods. Specifically, we focus on implementing an 8-neighbor connected moving average filter that considers the surrounding 9 pixels for each focal pixel.

Using strides, we can create a view of the original array that corresponds to the top row of the filter kernel. By applying a roll operation along the vertical axis, we can obtain the middle row of the kernel and add it to the initially created view. This process is repeated to obtain the bottom row of the kernel, and the sum of these rows is then divided by the filter size to calculate the average for each pixel.

To illustrate this approach, consider the following implementation:

import numpy, scipy

filtsize = 3
a = numpy.arrange(100).reshape((10, 10))
b = numpy.lib.stride_tricks.as_strided(a, shape=(a.size, filtsize), strides=(a.itemsize, a.itemsize))
for i in range(0, filtsize - 1):
    if i > 0:
        b += numpy.roll(b, -(pow(filtsize, 2) + 1) * i, 0)
filtered = (numpy.sum(b, 1) / pow(filtsize, 2)).reshape((a.shape[0], a.shape[1]))

In more general terms, defining a function that performs the rolling window operation along specified dimensions allows for the efficient implementation of moving average filters even in multi-dimensional arrays. However, it's important to note that while stride-based approaches offer advantages in specific cases, for complex multi-dimensional filtering tasks, specialized functions like those provided by the scipy.ndimage module may still offer superior performance.

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