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Abstract #2856

Single Image Super-Resolution using the Similarity of Sub-Images in FREBAS Transformed Space

satoshi ITO1

1Research Division of Intelligence and Infromation Science, Graduate School of Engineering, Utsunomiya University, Utsunomiya, Japan

In this paper, we propose a new fast image interpolation method involving super-resolution effects. We use FREBAS transform to obtain multi-directional multi-resolution sub-images. By using the similarity of sub-images between different size images, sub-images beyond the Nyquist frequency is estimated using the FREBAS transformed images corresponding scaling parameter. Experiments showed that obtained images have much more sharpened structure than super resolution method based on dictionary learning. PSNR and SSIM are improved and calculation cost is very small compared to learning based method.

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