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In this paper, we propose a robust neighbor embedding super-resolution (RNESR) scheme to generate a super-resolution (SR) image from a single low-resolution (LR) image. It utilizes histogram matching for selection of best training pair of images. This helps to learn co-occurrence prior to high-resolution (HR) image reconstruction. The global neighborhood size is computed from local neighborhood size,...
In this paper, super-resolution image is obtained from a single low-resolution image using dictionary learning approach. The original image is blurred and downsampled to the low-resolution image, and has to find the value which is lost during downsampling and trained with patches. Each patches of low-resolution image use that value of their respective high-resolution image during training of dictionary...
Resolution plays a crucial role for study of information in an image. Therefore to enhance the resolution of an image, there are so many techniques have been proposed with respect to the reference images. In this paper, we proposed a new scheme for single image super-resolution based on the neighbor embedding method. Many feature selection methods have been proposed for the learning based super-resolution...
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