## Tomography Demo

As an illustration of some techniques from my series of articles on tomography, I have added a tool that demonstrates basic scanning and reconstruction. **Go to the tool.** There are several… read more

**Go to the tool.** There are several… read more

This is the second of a two-part article on the PDART algorithm. It explains most of the nitty-gritty details. The PDART algorithm needs two extra input parameters to do its magic, a *threshold* and a *gray level*. After each SIRT iteration, each pixel of… read more

This article is a bit of an experiment. In it, I’ll try to explain PDART, an example of current algorithm research in computed tomography (PDART was published in 2011). What the algorithm does is, in a nutshell, a SIRT reconstruction with intermediate… read more

As a practical example of an iterative reconstruction algorithm, as introduced in “Tomography, Part 4: Algebra!”, I present the *SIRT* (*Simultaneous Iterative Reconstruction Technique*) algorithm. As in the mentioned article, I start from the system of linear equations… read more

*Compressed sensing* (or *compressive sensing*) is a relatively new technique that is becoming more and more important in image and signal acquisition. The term originates from image *compression*, where, in the classical workflow, images are first… read more

Here’s a challenge for you: Reconstruct the given sinogram using the the ASTRA Tomography Toolbox that I introduced in the previous article. You’ll have to figure out the exact meaning of a sinogram like this to be able to do that. For more information… read more

If you are into computed tomography (CT) from the perspective of algorithm development, or if you want to do the reconstruction yourself instead of using a standard software package (e.g., the one that was included with your scanner), you cannot ignore the *ASTRA Tomography Toolbox*. This toolbox was developed by… read more

Happy Valentine’s Day! This is a bit of a trick post; after luring you in with the cute heart, I’ll explain in this article how image filtering can be implemented in frequency space… read more

There is a fundamental difference between *adding Gaussian noise* and *applying Poisson noise*. In practice, people often talk about *adding* Poisson noise anyway, but this is not accurate. I will be looking at this from the image processing perspective in this article, and I’ll show purely visual examples. An application of this could be… read more

When taking a picture with a camera, the “true” image is *convolved* with the *point spread function* (PSF) of that camera, potentially producing a blurred image. *Deconvolution* is the process of removing the effect of this PSF again. In this article, I demonstrate that this is not an easy thing to do… read more