Discover and read the best of Twitter Threads about #noise2noise
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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…
