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FnS - Creating noise to remove noise

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If you have a question about this talk, please contact Mirco Giacobbe.

In photography, denoising is the postprocessing step where noise caused by inaccuracies in equipment is removed, and the best denoising methods implement deep learning. The most rudimental yet effective deep learning approach is to acquire a dataset containing images afflicted with similar noise patterns and their clean counterparts, then training a network to transform the former into an image that closely resembles latter, but such datasets are scarce. The premise of this project is that instead of clean counterparts we only need a model of exactly how the equipment is creating noise, one that can create its own noise. This allows us to train a network to transform a noisy input image into an estimate of its clean counterpart by checking, if noise were added back onto the estimate using our model, how closely it would resemble the original noisy input.

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