A important application in image enhancement where the spatial domain neighborhood operation is used image zooming. Zooming can be carried out using Replication and Interpolation.

**Replication:**

In replication, we simply replicate each pixel and then replicate each row. Consider the pseudo-image shown below.

We start from the first row. We replicate each pixel and then replicate each row. The first row looks now looks like

$\hspace{50mm}$ **1 1 2 2 3 3 4 4**

We now replicate this row to get

$\hspace{50mm}$ **1 1 2 2 3 3 4 4**

$\hspace{50mm}$ **1 1 2 2 3 3 4 4**

Performing this operation on the entire image we get

Hence a 4 x 4 image is zoomed to a 8 x 8 image. This method can be repeated to get bigger images. Remember, this is an image enhancement technique and hence no new data is added. In the zoomed pseudo-image, we observe that as we increase the size of the image, clusters of grey levels are formed which are disconcerting to the observer. Hence zooming increases the size of the image no doubt, but it also gives the image a patchy look.

Zooming by replication can be implemented on the computer by using a replication mask. The first step is to interlace the original image with zeros. This is known as zero interlacing. In this we add zeros after every pixel and then add a complete row of zeros. Adding zeros to every other pixel of the first row we get,

$\hspace{50mm}$ **1 0 2 0 3 0 4 0**

This is also known as zero interlacing the columns, as we add zeros at every other column. Now inserting a row full of zeros gives us. This is known as zero interlacing the rows as we add zeros at every other row.

$\hspace{50mm}$ **1 0 2 0 3 0 4 0**

$\hspace{50mm}$ **0 0 0 0 0 0 0 0**

This image is known as the zero interlaced image. On this image, we run a replication mask given below to get the zoomed the image.

The final zoomed image is shown below.