As some of you know, we are developing new noise reduction tools. As part of some early experiments, I created a synthetic image, composed by two linear gradients. To this, gaussian noise has being added to simulate a real, noisy image. Attached are the original image, and three noisy images, with different degrees of noise.

So, the challenge is this: Use all the tools you want to process the images (either inside PI, and with other software) and publish your best results. If you upload results done with different tools (for example, comparing your best results with GREYCstoration and ACDNR) that would be greatly appreciated. Please accompany your results with detailed description of the steps (if there are more than one), all the parameters that you adjusted, approximated time of execution, and some comments of yours about the sensibility of the parameters and easy of use.

Results should be evaluated by three criteria:

- Edge preservation

- Presence of artefacts (Gibbs effects, spurious pixels, staircase effects, etc).

- Smoothness of the gradients.

Images may be rescaled for display or comparison, after noise reduction. If you want to compute an error measurement, it should be the mean quadratic difference between the original and the denoised image, or the mean absolute difference.

Have fun!

I'll upload the results of the new algorithm in a few days.