Learning OpenCV

Image Pyramids

Image pyramids [Adelson84] are heavily used in a wide variety of vision applications. An image pyramid is a collection of images—all arising from a single original image—that are successively downsampled until some desired stopping point is reached. (Of course, this stopping point could be a single-pixel image!)

There are two kinds of image pyramids that arise often in the literature and in application: the Gaussian [Rosenfeld80] and Laplacian [Burt83] pyramids [Adelson84]. The Gaussian pyramid is used to downsample images, and the Laplacian pyramid (to be discussed shortly) is required when we want to reconstruct an upsampled image from an image lower in the pyramid.

To produce layer (i+1) in the Gaussian pyramid (we denote this layer G_i+1) from layer G_i of the pyramid, we first convolve G_i with a Gaussian kernel and then remove every even-numbered row and column. Of course, from this it follows immediately that each image is exactly one-quarter the area of its predecessor. Iterating this process on the input image G₀ produces the entire pyramid. OpenCV provides us with a method for generating each pyramid stage from its predecessor:

void cvPyrDown(
   IplImage*   src,
   IplImage*   dst,
   int         filter = IPL_GAUSSIAN_5x5
);

Currently, the last argument filter supports only the single (default) option of a 5-by-5 Gaussian kernel.

Similarly, we can convert an existing image to an image that is twice as large in each direction by the following analogous (but not inverse!) operation: ...