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Example-based super resolution to achieve fine magnification of low-resolution images

Super Resolution” (SR) is a technique to restore a low-resolution image into a clear, high resolution image. It is necessary to infer missing high frequency components in order to restore a low-resolution image to a high-resolution image correctly. This paper describes the learning-based SR technique that utilizes an example-based algorithm. This technique divides a large volume of training images into small rectangular pieces called “patches” and brings them together in a dictionary as patch pairs of low-resolution and high-resolution images. Experiments show impressive results that identify specific objects such as text characters and human faces etc.