Discover and read the best of Twitter Threads about #Noise2Void
Most recents (2)
We are pleased to announce that our #ZeroCostDL4Mic work is now published!
nature.com/articles/s4146…
And our @github
github.com/HenriquesLab/Z…
#DeepLearning and #Microscopy for all!
For the occasion, I’ve put a small Tweetorial together to explain what this is all about!
Thread 0/7
nature.com/articles/s4146…
And our @github
github.com/HenriquesLab/Z…
#DeepLearning and #Microscopy for all!
For the occasion, I’ve put a small Tweetorial together to explain what this is all about!
Thread 0/7
Thread 1/7
You can implement Deep Learning for microscopy in several ways:
1- Using local resources (fast GPUs)
2- Using cloud-computing
3- Using pre-trained models
We think that (1) is uncommon, and that (3) can be unreliable. So we do (2)!
You can implement Deep Learning for microscopy in several ways:
1- Using local resources (fast GPUs)
2- Using cloud-computing
3- Using pre-trained models
We think that (1) is uncommon, and that (3) can be unreliable. So we do (2)!
Thread 2/7
We built a platform using @GoogleColab and @ProjectJupyter notebooks that can perform training, quality control and prediction, all on the cloud and for free!
All of this dedicated to #microscopy !
We built a platform using @GoogleColab and @ProjectJupyter notebooks that can perform training, quality control and prediction, all on the cloud and for free!
All of this dedicated to #microscopy !
Thread
1/9 There's recently been much interest in the ML community in training denoisers w/o ground truth. Methods include #noise2noise and #noise2void. Main idea: clean/noisy pairs are unnecessary. Noisy images suffice to train. Let's to consider these works in proper context
1/9 There's recently been much interest in the ML community in training denoisers w/o ground truth. Methods include #noise2noise and #noise2void. Main idea: clean/noisy pairs are unnecessary. Noisy images suffice to train. Let's to consider these works in proper context
2/9 The notion that one can learn denoising without seeing clean/noisy pairs isn't new -- we've done it for decades: any modern denoiser (BM3D, bilateral, NLM) uses *only* the noisy image and has no explicit knowledge of what a clean image should look like.
3/9 A denoiser that computes its kernel (adaptively) based on the noisy image x is often expressible in pseudo-linear form W(x)*x where the rows of W(x) contain the weights. Computing W(x) is equivalent to computing an empirical estimate of a prior on x. ieeexplore.ieee.org/document/63759…
