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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.
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 ...
Image restoration and recolorization using artificial intelligence. Inpainting is a conservation process where damaged, deteriorated, or missing parts of an artwork are filled in to present a complete image. [1] This process is commonly used in image restoration.
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 ...
Image restoration may refer to: Conservation and restoration of photographs; Digital photograph restoration; Image restoration by artificial intelligence;
The purpose of early image processing was to improve the quality of the image. It was aimed for human beings to improve the visual effect of people. In image processing, the input is a low-quality image, and the output is an image with improved quality. Common image processing include image enhancement, restoration, encoding, and compression.
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