This post is a short tutorial on how to interpolate an image.

What does it mean to interpolate an image? Why would you want to do it? How can you do it?

Interpolation can be used for many purposes, but the most common one is to increase the resolution of an image. This trick is used in Photoshop and other programs for generating full-resolution images from smaller ones. Let’s see how that works!

In this article we will be looking at image interpolation and why it is important.

Image interpolation is a very important part of the image processing pipeline. It is quite easy to understand, but it turns out that the mathematics behind it are not trivial.

Before we start, I wanted to deal with some terminology and conventions that you might encounter if you were to look into the subject of image interpolation yourself.

* Interpolation is the process of constructing new information from what is known or measured on either side of a given point or area of interest. It helps us fill in gaps or blanks where data are missing.

* Interpolation can also refer to a computer graphics technique used to generate an image from a series of points which describe position, color, and other attributes. The technique uses those points within an algorithm to estimate intermediate values between them by using the known values in the surrounding area.

* Nearest-neighbor is a method for assigning or calculating values for new data points by using only the closest known values in surrounding areas.

This method was first used in digital images in 1972 by Arakawa and Wallace . They used it in order to estimate pixel values along the diagonal lines

Just to give you a little bit of background, I’m an artist and do a lot of work with images. Part of the process of generating images is interpolating them. This basically means taking two points, where one point is known, and then filling in all the space between those two points. It’s kind of like a function that you can use to map the data from one set to another set. So, for example, if you had a color spectrum and you wanted to map that onto another color spectrum, it would be very useful to be able to not only know what color is at certain points but also how much red or how much green or how much blue is involved at certain points.

Interpolation is a pretty important technique for a lot of people who work with image-generating algorithms, but there aren’t really any good ways to talk about it.

Though it is a very old technique, image interpolation has been recently the subject of intensive research. It is now the core technology behind (for instance) Google’s Deep Dream and Facebook’s Inceptionism. I find it useful to start with a quick review of what image interpolation is, how it works, and why it matters.

What Is Image Interpolation?

Image interpolation (also known as image resampling or just upsampling) is a way of modifying an image by generating new pixels from those in the original. A common use case would be resizing images for web publishing: one can upsample an image to double its size while preserving its sharpness using only simple arithmetic.

Image interpolation can also be used to do more sophisticated modifications, such as removing unwanted artifacts (e.g., a wide-angle lens) or improving sharpness (e.g., deblurring).

How Is Image Interpolation Implemented?

Interpolation algorithms can be divided into two categories: those that modify only the pixel values and those that modify both the pixel values and the colors associated with them. The simplest way to upsample is to simply replicate each pixel value in the same location in higher resolution; this method,

As you can see the interpolation is not a straight line, it is an increasing function of position. So how does this affect the image?

Well firstly we need to look at why interpolation is important and what exactly it is.

Interpolation is the process by which we find the values between discrete data points. See the image below which shows discrete data points and interpolated values.

The line between two data points, called the