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About Obtaining Super-Resolution Images by Combining Low-Resolution Images With High-Frequency Information Derivedfrom Training Images
(3) The document proposes a new algorithm to obtain super-resolution images by combining low-resolution images with high-frequency information derived from training images. It uses a nearest-neighbor search algorithm to select similar high-resolution images from a training dataset. It then uses a sparse representation algorithm to estimate weights to combine the high-frequency information of the selected images. The algorithm produces state-of-the-art super-resolution results by adding sharp edges from the high-frequency information of natural images to the low-resolution input image.
Detailed Information
| Author: | ['Anonymous Gl4IRRjzN'] |
|---|---|
| Publication Year: | 2013 |
| Pages: | 13 |
| Language: | English |
| Format: | |
| Price: | FREE |
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