Markov Random Fields for Super-resolution and Texture Synthesis
نویسنده
چکیده
Suppose we want to digitally enlarge a photograph. The input is a single, low-resolution image, and the desired output is an estimate of the high-resolution version of that image. This problem can be phrased as one of “image interpolation”: we seek to interpolate the pixel values between our observed samples. Image interpolation is sometimes called super-resolution, since we are estimating data at a resolution beyond that of the image samples. In contrast with multi-image super-resolution methods, where a high-resolution image is inferred from a video sequence, we are interested in estimating high-resolution images from a single low-resolution example [10].
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