This tutorial will teach you how image interpolation works. You’ll learn about the different interpolation methods used to regenerate a low res image, and why aliasing happens in these cases.

Image interpolation refers to reconstructing a high resolution image from a low-resolution version. When you’re working with video games or animations, the video card must have enough memory to store the entire screen, which can take quite some space. So it’s not uncommon for graphics cards to save a lower-resolution version of the screen, and then run an algorithm that regenerates the missing pixels.

This blog post is part of our ongoing series explaining the underlying science behind animation techniques. We explain here how image interpolation works and what are the main reasons behind aliasing artifacts when performing this type of operation. The explanation requires basic knowledge of linear algebra and fourier transform theory and assumes familiarity with the concepts of aliasing, sampling rate, and frequency spectrum.

image interpolation is the method of generating a pixelated image from a single high resolution image. For example, say you have a black and white photo of your dog. You then use software to make it look like it was hand drawn on graph paper using colored pencils. There are many different methods of doing this, but the basic concept remains the same across all of them.

In order to do this, we need to know how the photo was taken in the first place. Most cameras and phones today use what is called a Bayer Pattern mosaic filter, as shown below:

If you look closely at this pattern you will see that there are twice as many green pixels as red or blue; furthermore there are twice as many green pixels in each row than there are columns. This makes sense because on average our eyes are more sensitive to green light than red or blue.

This gives us an idea of how the picture was taken. The camera takes the picture through a red filter, then a green filter, then a blue filter (hence RGGB). The picture looks something like this:

To interpolate or regenerate our picture we need to be able to figure out what each pixel represents when it is combined with its neighbors. We can see that on average each pixel

The simplest way to imagine interpolation is as a process of gradually adding detail. The lower resolution your source, the more you need to interpolate.

This is useful for example when you have an image that has been shrunk to fit a smaller screen with poor resolution. By generating new pixels between existing ones, we can create a smoother image.

One of the most common methods used to do this is called nearest neighbor interpolation. This method is quite simple and works by comparing each pixel to its closest neighbor and simply replicating the value that was already there in its place.

This method works great when you want a fast, cheap solution, but it does come with some limitations. For example, if you want your image to retain sharp lines you’ll find that nearest neighbor interpolation does not work well in this case.

This is a basic blog on image interpolation methods and their application and is intended to be read by both novice and experienced graphics programmers. Image interpolation is the technique of generating a new image from a set of existing samples in an efficient manner, i.e. without having to process all of the pixels in the image. Image interpolation can be achieved through one of two methods: linear or non-linear interpolation. Linear interpolation is generally simpler and faster but may result in distortion of the image particularly at high magnifications, whereas non-linear methods produce more pleasing results but at a cost of increased computational complexity and memory usage.

Non-Linear Interpolation Techniques

Bilinear Interpolation

The simplest form of non-linear interpolation is bilinear interpolation which simply averages each pixel with its neighbours using the following equation:

P'(x,y) = (1/2)(P(x+1,y) + P(x,y))

Image interpolation is the process of taking one or more images, and producing a new image that is smoother, has more detail, or both.

The most common use of image interpolation is with digital cameras. When you take a picture, you are really just taking a bunch of samples of the image at regular intervals. Once you have captured your image, you can then smooth it out by sliding those samples over their neighbors and filling in the gaps. This gives you an image that has better detail than your original sample set.

Interpolation can also be used to remove noise from an image. There are many ways to do this, but they all involve generating a new pixel based on several of its neighbors. This works because the kind of noise we typically deal with tends to be much more random than the structure of an image. By averaging a few neighboring pixels together, you can get rid of some of the random noise without losing any real details.

Image interpolation can also be used for artistic effect. A painting program like Photoshop does this when it “blurs” an image. It actually does not blur the image at all; it uses interpolation to generate new pixels along each edge in such a way as to cause them to blend nicely with their neighbors.

Interpolation is one of the most basic image manipulations; it’s what you do when you take a low resolution image and make it look like a high resolution image. With interpolation, you are essentially guessing where the missing pixels should be, and plugging them in.

Tiles vs Bezier curves

There are two basic ways to interpolate an image. They both involve taking a source image, and using the source pixels to guess where the new pixels should go. The difference between them is in how they use the source pixels.

To place the new pixel, first draw a line from the center of each of the surrounding four pixels. If those lines meet at a point, then that point is where you put your new pixel. This is called Bresenham’s line algorithm . It works well if your source image is only using 4 colors or less , because it uses very little processing power — basically just drawing lines and looking for intersections.

It’s used by putting colored dots in any empty space on your canvas (usually white). You start by choosing any empty space as your first pixel; say, a blue dot on a white canvas. Then you draw a line from every surrounding pixel to that first blue dot, and see if they all