While reading about image recognition algorithms, I learned about a method of band-pass filtering I hadn't seen before. The Difference of Gaussians method can be used to band-pass filter an image quickly and easily. Instead of convolving the image with a band-pass kernel, the Difference of Gaussians methods uses two low pass filters and subtracts the two.
You start by blurring the image using a gaussian kernel, then subtract the blurred image it from a second less blurred version of the original. The result is an image with only features between the two blur levels. The two levels of blur used in the subtraction step can be varied to give different band pass limits.
This method can be effectively used for edge detection because it cuts down high frequency noise by subtracting a less blurred image. That means that noise in the image doesn't get treated as an edge. Apparently there are common blur levels that cause the Difference of Guassians method to approximate response of ganglion cells (light sensing nerve cluster in the eye)to light that falls on or near them.