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Because noise can interfere with downstream analysis, image denoising hascome to occupy an important place in the image processing toolbox. The mostaccurate state-of-the-art denoisers typically train on a representativedataset. But gathering a training set is not always feasible, so interest hasgrown in blind zero-shot denoisers that train only on the image they aredenoising. The most accurate blind-zero shot methods are blind-spot networks,which mask pixels and attempt to infer them from their surroundings. Othermethods exist where all neurons participate in forward inference, however theyare not as accurate and are susceptible to overfitting. Here we present ahybrid approach. We first introduce a semi blind-spot network where the networkcan see only a small percentage of inputs during gradient update. We thenresolve overfitting by introducing a validation scheme where we split pixelsinto two groups and fill in pixel gaps using domino tilings. Our methodachieves an average PSNR increase of $0.28$ and a three fold increase in speedover the current gold standard blind zero-shot denoiser Self2Self on syntheticGaussian noise. We demonstrate the broader applicability of Pixel Domino Tilingby inserting it into a preciously published method.

Domino Denoise: An Accurate Blind Zero-Shot Denoiser using Domino Tilings