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  2. Let's Enhance - Wikipedia

    en.wikipedia.org/wiki/Let's_Enhance

    Let's Enhance [1] is a Ukrainian start-up [2] which develops an online service driven by artificial intelligence which allows improving images and zooming them without losing quality. [3] According to the developers, [4] they used the super-resolution technology of machine learning. The neural network, trained on a large base of real ...

  3. FaceApp - Wikipedia

    en.wikipedia.org/wiki/FaceApp

    FaceApp is a photo and video editing application for iOS and Android developed by FaceApp Technology Limited, a company based in Cyprus. [1] The app generates highly realistic transformations of human faces in photographs by using neural networks based on artificial intelligence.

  4. Image restoration by artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Image_restoration_by...

    Image restoration can be broadly categorized into two main types: spatial domain and frequency domain methods. Spatial domain techniques operate directly on the image pixels, while frequency domain methods transform the image into the frequency domain using techniques such as the Fourier transform, where restoration operations are performed.

  5. Facial recognition system - Wikipedia

    en.wikipedia.org/wiki/Facial_recognition_system

    Facial recognition software at a US airport Automatic ticket gate with face recognition system in Osaka Metro Morinomiya Station. A facial recognition system [1] is a technology potentially capable of matching a human face from a digital image or a video frame against a database of faces.

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    mail.aol.com

    Get AOL Mail for FREE! Manage your email like never before with travel, photo & document views. Personalize your inbox with themes & tabs. You've Got Mail!

  7. DeepDream - Wikipedia

    en.wikipedia.org/wiki/DeepDream

    The software is designed to detect faces and other patterns in images, with the aim of automatically classifying images. [10] However, once trained, the network can also be run in reverse, being asked to adjust the original image slightly so that a given output neuron (e.g. the one for faces or certain animals) yields a higher confidence score.

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