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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. Image restoration by artificial intelligence - Wikipedia

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

   Image enhancement techniques (like contrast stretching or de-blurring by a nearest neighbor procedure) provided by imaging packages use no a priori model of the process that created the image. With image enhancement noise can effectively be removed by sacrificing some resolution, but this is not acceptable in many applications.

4. Image editing - Wikipedia

   en.wikipedia.org/wiki/Image_editing

   Image editors can change the pixels to enhance the image in many ways. The pixels can be changed as a group or individually by the sophisticated algorithms within the image editors. This article mostly refers to bitmap graphics editors, which are often used to alter photographs and other raster graphics.

5. Image scaling - Wikipedia

   en.wikipedia.org/wiki/Image_scaling

   Image scaling can be interpreted as a form of image resampling or image reconstruction from the view of the Nyquist sampling theorem.According to the theorem, downsampling to a smaller image from a higher-resolution original can only be carried out after applying a suitable 2D anti-aliasing filter to prevent aliasing artifacts.

6. Kernel (image processing) - Wikipedia

   en.wikipedia.org/wiki/Kernel_(image_processing)

   In image processing, a kernel, convolution matrix, or mask is a small matrix used for blurring, sharpening, embossing, edge detection, and more.This is accomplished by doing a convolution between the kernel and an image.

7. 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.