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…
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…
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…
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…
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…
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…
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…
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…
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…
After computing The PSF of a Pinhole Camera and showing Some Wacky Pinholes and Their PSF in previous articles, the next step towards ever more realistic PSFs is adding color. A DSLR camera has detector pixels that are sensitive to red, green, and blue, and, since the size of the PSF depends on…