Aydin Use-Cases, and the Art & Science of Image Denoising.

While designing Aydin, we came to the realisation that there is no silver bullet, there is not a single algorithm nor single set of parameters that can reliably denoise all images. More fundamentally, ‘image denoising’ is not a singular problem, but a multiplicity of challenges. There are many different kinds of image noises and related degradations and it requires a lot of know-how to both understand this diversity and know what to do for each kind of noise. This diversity comes from the inherent diversity of imaging modalities: different instruments, optics, detectors, contrast mechanisms, photo-chemistry, etc… This extends well beyond just optical images into other imaging modalities – it essentially holds true for any mechanism that generates images and more generally any multi-dimensional array-like measurement.

The use-cases presented below are the ideal starting point to understand how to choose among denoising algorithms, among pre- and post- processing steps, and how to adjust their parameters. We are actively populating this list with more datasets, and further improving this material, please check this page for updates!

  1. Denoising Basics with Aydin

  2. Denoising Spinning-Disk Confocal Microscopy Images with Aydin

Note: We are always interested in learning about new challenging images, please contact us by filling an issue here if you face difficulties or have a dataset that resists our methods.