Noise-free digital photos

Photon emission and sensing are inherently random physical processes and contribute to the randomness in the output of the imaging sensor. This signal-dependent noise can be approximated through a Poisson distribution. In addition, there are various signal-independent noise sources involved in the image capturing chain, arising from the physical properties and imperfections of the imaging hardware. The noise attributed to these sources is typically modelled collectively as additive white Gaussian noise. There are three methods for modelling noise present in digital images: Gaussian, Poisson, or Poisson-Gaussian.

Image denoising aims at removing or attenuating this noise from the captured image, in order to provide an estimate of the underlying ideal noise-free image. For simplicity, denoising algorithms often assume the noise to be Gaussian, and ignore the signal-dependency. However, in an image corrupted by signal-dependent noise, the noise variance is typically not constant and varies with the expectation of the pixel value. To remove signal-dependent noise, we can either design an algorithm specifically for the particular noise model or use an indirect three-step variance-stabilization approach. In the indirect approach, the noisy image is first processed with a variance-stabilizing transformation (VST), which renders the noise approximately Gaussian with a known constant variance. Then the transformed image is denoised with a Gaussian denoising algorithm, and finally an inverse VST is applied to the denoised data, providing us with the final estimate of the noise-free image.

Public defence of a doctoral dissertation on Friday, 1 March

The doctoral dissertation of MA Markku Mäkitalo in the field of signal processing titled”Exact Unbiased Inverse of the Anscombe Transformation and its Poisson-Gaussian Generalization" will be publicly examined at the Faculty of Computing and Electrical Engineering of Tampere University of Technology (TUT) in room TB190 in the Tietotalo building (address: Korkeakoulunkatu 1, Tampere, Finland) at 12:00 on Friday, 1 March 2013.

The opponents will be Professor Rebecca Willett (Duke University) and Professor Janne Heikkilä (University of Oulu). Adjunct Professor Alessandro Foi from the Department of Signal Processing of TUT will act as Chairman.

Markku Mäkitalo (29) comes from Kemi and currently lives in Tampere. He works as a researcher at the Department of Signal Processing of TUT.

Further information:

Markku Mäkitalo, tel. +358 40 198 1312 markku.makitalo@tut.fi
The dissertation is available online at: http://URN.fi/URN:ISBN:978-952-15-3039-5