For instance, a picture of a person with a missing face conveys no meaning to the network except changing values for pixels. These patches can be considered a hyperparameter required by the network since the network has no way of discerning what actually needs to be filled in. The basic workflow is as follows: feed the network an input image with “holes” or “patches” that need to be filled. Here’s one of them below, with a big chunk of my face missing and the neural network restoring it again in a matter of seconds, albeit making me look like I just got out of a street fight. I tried it on a few pictures lying around on my desktop. Simply drag and drop any image file, erase a portion of it with the cursor and watch how the AI patches it up. Go ahead and try it out yourself, with NVIDIA’s web playground that demonstrates how their network fills in a missing portion for any image. Simply feed a damaged image to a neural network and receive the corrected output. This official definition of inpainting on Wikipedia already takes into account the use of “sophisticated algorithms” that do the same work of manually overwriting imperfections or repairing defects but in a fraction of the time.Īs deep learning technologies progress further, however, the process of inpainting has become automated in so complete a manner that these days, it requires no human intervention at all.
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In the digital world, inpainting refers to the application of sophisticated algorithms to replace lost or corrupted parts of the image data. In the museum world, in the case of a valuable painting, this task would be carried out by a skilled art conservator or art restorer. Inpainting is the process of reconstructing lost or deteriorated parts of images and videos. This view from my school would be just the sort of thing Inpainting could improve.