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2. Image restoration by artificial intelligence - Wikipedia

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

   Digital photograph restoration and colorization using artificial intelligence Image restoration is the operation of taking a corrupt/noisy image and estimating the clean, original image. Corruption may come in many forms such as motion blur , noise and camera mis-focus. [ 1 ]

3. Digital photograph restoration - Wikipedia

   en.wikipedia.org/wiki/Digital_photograph_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.

4. Inpainting - Wikipedia

   en.wikipedia.org/wiki/Inpainting

   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]

5. Image restoration - Wikipedia

   en.wikipedia.org/wiki/Image_restoration

   Image restoration may refer to: Conservation and restoration of photographs; Digital photograph restoration; Image restoration by artificial intelligence;

6. Digital image processing - Wikipedia

   en.wikipedia.org/wiki/Digital_image_processing

   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.

7. Shrinkage Fields (image restoration) - Wikipedia

   en.wikipedia.org/wiki/Shrinkage_Fields_(image...

   The restored image is predicted from a corrupted observation after training on a set of sample images .. A shrinkage (mapping) function () = =, ⁡ (()) is directly modeled as a linear combination of radial basis function kernels, where is the shared precision parameter, denotes the (equidistant) kernel positions, and M is the number of Gaussian kernels.