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2. Unsharp masking - Wikipedia

   en.wikipedia.org/wiki/Unsharp_masking

   The same differencing principle is used in the unsharp-masking tool in many digital-imaging software packages, such as Adobe Photoshop and GIMP. [1] The software applies a Gaussian blur to a copy of the original image and then compares it to the original. If the difference is greater than a user-specified threshold setting, the images are (in ...

3. Comparison gallery of image scaling algorithms - Wikipedia

   en.wikipedia.org/wiki/Comparison_gallery_of...

   An image size can be changed in several ways. Consider resizing a 160x160 pixel photo to the following 40x40 pixel thumbnail and then scaling the thumbnail to a 160x160 pixel image. Also consider doubling the size of the following image containing text.

4. 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.

5. Image restoration by artificial intelligence - Wikipedia

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

   Nowadays, photo restoration is done using digital tools and software to fix any type of damage images may have and improve the general quality and definition of the details. Types of AI corrections [ edit ]

6. Edge enhancement - Wikipedia

   en.wikipedia.org/wiki/Edge_enhancement

   Edge enhancement is an image processing filter that enhances the edge contrast of an image or video in an attempt to improve its acutance (apparent sharpness).. The filter works by identifying sharp edge boundaries in the image, such as the edge between a subject and a background of a contrasting color, and increasing the image contrast in the area immediately around the edge.

7. Compression artifact - Wikipedia

   en.wikipedia.org/wiki/Compression_artifact

   The lower the bit rate, the more coarsely the coefficients are represented and the more coefficients are quantized to zero. Statistically, images have more low-frequency than high-frequency content, so it is the low-frequency content that remains after quantization, which results in blurry, low-resolution blocks. In the most extreme case only ...