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The future of image restoration is likely to be driven by developments in deep learning and artificial intelligence. Convolutional neural networks (CNNs) have shown promising results in various image restoration tasks, including denoising, super-resolution, and inpainting.
Deep image prior is a type of convolutional neural network used to enhance a given image with no prior training data other than the image itself. A neural network is randomly initialized and used as prior to solve inverse problems such as noise reduction , super-resolution , and inpainting .
Image restoration may refer to: Conservation and restoration of photographs; Digital photograph restoration; Image restoration by artificial intelligence;
Digital photograph restoration uses image editing techniques to remove undesired visible features, such as dirt, scratches, or signs of aging. People use raster graphics editors to repair digital images, or to add or replace torn or missing pieces of the physical photograph. Unwanted color casts are removed and the image's contrast or ...
Various deep learning approaches have been proposed to achieve noise reduction [46] and such image restoration tasks. Deep Image Prior is one such technique that makes use of convolutional neural network and is notable in that it requires no prior training data. [47]
Neural style transfer (NST) refers to a class of software algorithms that manipulate digital images, or videos, in order to adopt the appearance or visual style of another image. NST algorithms are characterized by their use of deep neural networks for the sake of image transformation. Common uses for NST are the creation of artificial artwork ...
Synthetic media (also known as AI-generated media, [1] [2] media produced by generative AI, [3] personalized media, personalized content, [4] and colloquially as deepfakes [5]) is a catch-all term for the artificial production, manipulation, and modification of data and media by automated means, especially through the use of artificial intelligence algorithms, such as for the purpose of ...
The generator is decomposed into a pyramid of generators =, with the lowest one generating the image () at the lowest resolution, then the generated image is scaled up to (()), and fed to the next level to generate an image (+ (())) at a higher resolution, and so on. The discriminator is decomposed into a pyramid as well.
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