added notebook about downsampling in combination with denoising

[thawn]

In this notebook, several ways to down-scale image data are compared. Start with relatively simple and fast methods, moving to algorithms that combine down-scaling and denoising. Finally, the algorithms are benchmarked on a M1 Macbook Pro.

Summary of the findings:

Denoising before down-scaling significantly improves signal-to-noise, without loosing more detail than down-scaling alone. The additional computing time required for denoising is therefore well invested - particularly, if efficient algorithms such as skimage.transform.downscale_local_mean() or GPU acceleration are used.

closes #39

[haesleinhuepf]

Hi Till @thawn ,

that's amazing, big thanks for this! It's perfect as it is. Just next time try to write text that's a bit more concise. Maybe, also not all examples are necessary 😉 But leave this as it is, it's great!

Two minor requests:

• Please add the notebook to the TOC: https://github.com/haesleinhuepf/BioImageAnalysisNotebooks/blob/31e2ccce034eccb818a668710f2e73dda51b4d15/docs/_toc.yml#L104
• optional, feel free to add yourself to the list of authors: https://github.com/haesleinhuepf/BioImageAnalysisNotebooks/blob/31e2ccce034eccb818a668710f2e73dda51b4d15/docs/_config.yml#L2

Thanks!

Best, Robert

[thawn]

Done :-)