SimWdm
commented
4 days ago Hi @amineuron,
Thanks for trying DeepDeWedge! I am glad to hear that you find it useful!
I have also noticed that DeepDeWedge sometimes removes some of the high frequency components to produce cleaner tomograms.
My hypothesis is that this is because some high frequency details are "below the noise level", so the network cannot learn to produce these details based on the noisy inputs and targets it is trained on. In other words: The network sometimes has a hard time learning to distinguish between high-frequency detail and noise, and ends up removing parts of the detail to produce a cleaner-looking tomogram. Such a trade-off between cleaner-looking images and removal of high-frequency components is often observed with denoising algorithms.
Unfortunately, I don't have a very satisfactory answer as to what you can do to solve the problem. However, I think the following might help to some extent: You could check the refined tomogram every few epochs before the model fitting has converged (e.g. every 10 epochs, but the exact number depends on how fast the model fitting converges). It is possible that in these earlier epochs, the denoising is not strong enough to remove the high frequency details because the network has not yet learned to do so. However, earlier models may also be less effective at removing noise in low frequency detail and lacking wedge filling, so (assuming my hypothesis is correct) you would need to manually find a sweet spot.
I've never tried this approach, but I'd be very interested to hear if it works for you!
Best, Simon
Hi,
Thank you for developing this tool—it's been working very well, especially in handling the organell missing wedge. However, its clear that most of the high-frequency side of data seems to be lost during the denoising process. I’m wondering if there's a way to retain these finer details.
Do you have any recommendations for preserving high-frequency information?
Is there a way to adjust the parameters to maintain the high-frequency data while still benefiting from the denoising?
Could reducing subtomo_overlap help in retaining details, or would that not make much of a difference due to the final averaging step with the two half-maps?
Any guidance on this would be greatly appreciated!