Posts Tagged ‘filter’

Mathematical Morphology and colour images

Sunday, June 23rd, 2013

We recently organized the 11th International Symposium on Mathematical Morphology here in Uppsala. I’m very happy with how the event turned out, and we got lots of positive comments from participants, so all our hard work paid off. We had a nice turnout, very interesting presentations, and lots of discussions. And I’m now the editor of a book containing all the papers presented.

There seem to be several trending topics in the ISMM community at the moment. One of those is the application of Mathematical Morphology to colour images. People have been working on this topic for a while now, and still there is no optimal solution. This year, three very different methods were presented to try and solve this problem. But what is this problem about?


Evaluating noise filters

Saturday, September 1st, 2012

Most of the new papers that I come across that propose a new or improved way of filtering out noise from images use the Peak Signal-to-Noise Ratio (PSNR) as a means to evaluate their results. It has been shown again and again that this is not a good way of evaluating the performance of a filter. When people compute the PSNR for a filtered image, what they actually do is compare this filtered image to an undistorted one (i.e. known ground truth). This is very different from what the name PSNR implies: the ratio of peak signal power to noise power. Of course, that is something that cannot be measured: if we’d be able to separate the noise from the signal and measure the power of the two components, then we wouldn’t need to write so many papers about filters that remove noise! So instead, the typical PSNR measure in image analysis uses the difference between the filtered image and the original (supposedly noise-free) image, calls this difference the noise, and computes its power (in dB):