Enhance Example-Based Super Resolution to Achieve Fine Magnification of Low Resolution Images Using Neighbour Embedding Method
Journal Title: International Journal of Modern Engineering Research (IJMER) - Year 2014, Vol 4, Issue 5
Abstract
Images with high resolution (HR) often required in most electronic imaging applications. There are two types of resolution first is high resolution and other one is low resolution. Now high resolution means pixel density with in an image is high and low resolution means pixel density with in an image is low. Therefore high resolution image can offer more detail compare to low resolution image that may be critical in many application. Super resolution is the process to obtain high resolution image from one or more low resolution images. Here in paper explain such robust methods of image super resolution. 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 patch pairs of low-resolution and high-resolution images are stored in dictionary. After that there are low resolution patch is extracted from the input images. The most alike patch pair is searched in the dictionary to synthesize high resolution image using the searched high resolution patch in the pair.
Authors and Affiliations
Nisarg Patel 1 Rahul Joshi 2
Keywords
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