Interpolation (sometimes called resampling) is an imaging method to increase (or decrease) the number of pixels in a digital image. Some digital cameras use interpolation to produce a larger image than the sensor captured or to create digital zoom. Virtually all image editing software support one or more methods of interpolation. How smoothly images are enlarged without introducing jaggies depends on the sophistication of the algorithm.
The examples below are all 450% increases in size of this 106 x 40 crop from an image.
Nearest Neighbor Interpolation
Nearest neighbor interpolation is the simplest method and basically makes the pixels bigger. The color of a pixel in the new image is the color of the nearest pixel of the original image. If you enlarge 200%, one pixel will be enlarged to a 2 x 2 area of 4 pixels with the same color as the original pixel. Most image viewing and editing software use this type of interpolation to enlarge a digital image for the purpose of closer examination because it does not change the color information of the image and does not introduce any anti-aliasing. For the same reason, it is not suitable to enlarge photographic images because it increases the visibility of jaggies.
|Nearest Neighbor Interpolation|
Bilinear Interpolation determines the value of a new pixel based on a weighted average of the 4 pixels in the nearest 2 x 2 neighborhood of the pixel in the original image. The averaging has an anti-aliasing effect and therefore produces relatively smooth edges with hardly any jaggies.
Bicubic interpolation is more sophisticated and produces smoother edges than bilinear interpolation. Notice for instance the smoother eyelashes in the example below. Here, a new pixel is a bicubic function using 16 pixels in the nearest 4 x 4 neighborhood of the pixel in the original image. This is the method most commonly used by image editing software, printer drivers and many digital cameras for resampling images. As mentioned in my review, Adobe Photoshop CS offers two variants of the bicubic interpolation method: bicubic smoother and bicubic sharper.
|Bicubic Smoother||Bicubic||Bicubic Sharper|
Fractal interpolation is mainly useful for extreme enlargements (for large prints) as it retains the shape of things more accurately with cleaner, sharper edges and less halos and blurring around the edges than bicubic interpolation would do. An example is Genuine Fractals Pro from The Altamira Group.
There are of course many other methods of interpolation but they're seldom seen outside of more sophisticated image manipulation packages.
This article is written by Vincent Bockaert,
author of The 123 of digital imaging Interactive Learning Suite
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