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

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

Noise2Fast: Fast Self-Supervised Single Image Blind Denoising

In the last several years deep learning based approaches have come to dominate many areas of computer vision, and image denoising is no exception. Neural networks can learn by example to map noisy images to clean images. However, access to noisy/clean or even noisy/noisy image pairs isn't always readily available in the desired domain. Recent approaches have allowed for the denoising of single noisy images without access to any training data aside from that very image. But since they require both training and inference to be carried out on each individual input image, these methods require significant computation time. As such, they are difficult to integrate into automated microscopy pipelines where denoising large datasets is essential but needs to be carried out in a timely manner. Here we present Noise2Fast, a fast single image blind denoiser. Our method is tailored for speed by training on a four-image dataset produced using a unique form of downsampling we refer to as "checkerboard downsampling". Noise2Fast is faster than all tested approaches and is more accurate than all except Self2Self, which takes well over 100 times longer to denoise an image. This allows for a combination of speed and flexibility that was not previously attainable using any other method.