Image Processing

The PDART Algorithm, Part 2

SIRT (left) and mask (right) at iteration 8

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

Submitted on 24 August 2014

The PDART Algorithm, Part 1

Phantom image (left) and SIRT reconstruction (right)

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

Submitted on 11 August 2014

Compressed Sensing

Compressed sensing example with stars. Original image (left), measurements (middle), reconstructed image (right).

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

Submitted on 1 June 2014

Valentine Filtering

Heart

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

Submitted on 12 February 2014

Gaussian Noise is Added, Poisson Noise is Applied

Poisson and Gaussian noise, pixel values 0 to 5

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

Submitted on 11 January 2014

Why is Deconvolution Difficult?

Lena convolved with the Airy pattern

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

Submitted on 24 December 2013

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