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Video Do not worry if the lighting is a little dazzled in your photos, AI can fix it.
Computer scientists from Nvidia, Aalto University, and the Massachusetts Institute of Technology formed a network of neurons to restore images spoiled by noise patches. Computer vision algorithms are already automatically used to enhance images taken on smartphones like Pixel 2 or iPhone X, but that goes further.
But with this new technique, the workout process is slightly different. Instead of feeding the neural networks of a pair of images, where one is of high quality and the other is fuzzy, the model, nicknamed "noise2noise", can learn to clean images without the need to see high resolution examples.
Basic statistical reasoning to report reconstruction by machine learning – learn to map corrupt observations to clean signals – with a simple and powerful conclusion: in some common circumstances, it is possible to learn to restore the signals without ever observing the proper signals "Abstract
The theoretical basis for explaining why this works is a bit difficult to understand. The more traditional techniques use low resolution and high resolution image pairs, learn to minimize the loss function by estimating the difference in pixel values between the two images.
Pixels can take a wide range of values to recreate an image. image sharper, and over time, the neural network learns to average these values. The same idea can be applied when training on spoiled picture pairs, though the difference between the pixel values in the two pictures is relatively similar to those between a clean and fuzzy picture.
"This implies that we can, in principle, corrupt the training targets of a neural network with a noise of zero average without changing what the network learns," the paper explains.
Training with Technology
The team trained its noise2noise model on 50,000 images from the ImageNet dataset. a random distribution of noise to each image. The system must estimate the magnitude of the noise on the photo and remove it.
It has been tested on three sets of data with images of buildings, people and medical resonance imaging. Here is a video with some results.
Youtube video
The model will not cure all imperfections, however. It can not restore objects just out of frame or reposition the photo to get the best angles. But it is useful when there are not enough examples of high resolution to train like good images of galaxies or planets. "There are several real-world situations where getting clean driving data is difficult: low-light photography," our proof-of-concept demonstrations pave the way for significant potential benefits in these applications by eliminating the need from a potentially painstaking collection of own data.Of course, there is no free lunch – we can not learn to enter features that are not present in the input data – but this also applies to training with clean targets. "
The research is presented at the International Learning in Sweden Conference this week. ®
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