Image Processing – Nearest Neighbour Interpolation

In the previous blog, we discussed image interpolation, its types and why we need interpolation. In this blog, we will discuss the Nearest Neighbour, a non-adaptive interpolation method in detail.

Algorithm: We assign the unknown pixel to the nearest known pixel.

Let’s see how this works. Suppose, we have a 2×2 image and let’s say we want to upscale this by a factor of 2 as shown below.

Let’s pick up the first pixel (denoted by ‘P1’) in the unknown image. To assign it a value, we must find its nearest pixel in the input 2×2 image. Let’s first see some facts and assumptions used in this.

Assumption: a pixel is always represented by its center value. Each pixel in our input 2×2 image is of unit length and width.

Indexing in OpenCV starts from 0 while in matlab it starts from 1. But for the sake of simplicity, we will start indexing from 0.5 which means that our first pixel is at 0.5 next at 1.5 and so on as shown below.

So for the above example, the location of each pixel in input image is {’10’:(0.5,0.5), ’20’:(1.5,0.5), ’30’:(0.5,1.5), ’40’:(1.5,1.5)}.

After finding the location of each pixel in the input image, follow these 2 steps

  1. First, find the position of each pixel (of the unknown image) in the input image. This is done by projecting the 4×4 image on the 2×2 image. So, we can easily find out the coordinates of each unknown pixel e.g location of ‘P1’ in the input image is (0.25,0.25), for ‘P2’ (0.75,0.25) and so on.
  2. Now, compare the above-calculated coordinates of each unknown pixel with the input image pixels to find out the nearest pixel e.g. ‘P1′(0.25,0.25) is nearest to 10 (0.5,0.5) so we assign ‘P1’ value of 10. Similarly, for other pixels, we can find their nearest pixel.

The final result we get is shown in figure below:

This is the fastest interpolation method as it involves little calculation. This results in a pixelated or blocky image. This has the effect of simply making each pixel bigger

Application: To resize bar-codes.

Shortcut: Simply duplicate the rows and columns to get the interpolated or zoomed image e.g. for 2x, we duplicate each row and column 2 times.

In the next blog, we will discuss Bi-linear interpolation method. Hope you enjoy reading.

If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.

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