Posts Tagged ‘noise’

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):

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Dithering

Friday, January 7th, 2011

First of all: Happy New Year!

Over the holidays I’ve been learning about dithering, the process of creating the illusion of many grey levels using only black and white dots. This is used when displaying an image on a device with fewer than the 64 or so grey levels that we can distinguish, such as an ink-jet printer (which prints small, solid dots), and also when quantizing an image to use a colour map (remember the EGA and VGA computer displays?). It turns out that this is still an active research field. I ran into the paper Structure-aware error diffusion, ACM Transactions on Graphics 28(5), 2009, which improves upon a method presented a year earlier, which in turn improved on the state of the art by placing dots to optimize the appearance of thin lines. This got me interested in the basic algorithms, which I had never studied before. Hopefully this post will give an understanding of dithering and its history.

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DIPimage 2.1 released

Tuesday, August 4th, 2009

I’m pleased to announce we’ve released a new version of DIPimage and DIPlib, with several relevant changes. For a full list of changes, please see the DIPlib website, here I’ll review the most salient changes.

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The sources of noise

Wednesday, July 8th, 2009

Noise in images perturbs measurements and makes the lives of image analysis people interesting. There are many sources of noise, depending on the imaging modality: photon noise, thermal noise, electronic noise, quantization noise, etc. I have just found out about another source of noise: etheric entities. Apparently these etheric entities appear especially in “certain photos of human/reptilian hybrids such as Bush or Obama,” and are completely indistinguishable from other types of noise. I’m not looking forward to test my algorithms against this form of noise.

Cryptography or steganography?

Friday, May 8th, 2009

RSA and DES keys keep growing in length, to keep up with increasing computational power. Keys we were using 15 years ago are laughable now. And according to a nice graph in April’s IEEE Spectrum, ridiculous amounts of computing power are cheaper than ever. I wonder how long before people give up on encryption altogether.

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