In current color image super-resolution methods, super-resolution based on sparse representation achieves state-of-the-art performance. However, the exploited sparse repre-sentation models deal with the color images as independent channel planes. Consequently, these approaches process the color pixels as scalar quantity, lacking of accuracy in describ-ing inter-relationship among color channels. In this paper, we propose a quaternion-based online dictionary learning method and solve color image super-resolution by employ-ing a quaternion-based sparse representation model. This sparse representation model implements color image super-resolution in a kind of vectorial reconstruction, effectively accounting for both luminance and chrominance geometry in images. The proposed color image super-resolution method can better describe the inter-channel changes. In the case that changing lighting conditions affect color more than the luminance perception, it can obtain superior performance comparing to the methods based on monochromatic sparse models with 1dB improvement. Index Terms — Quaternion, super-resolution, sparse rep-resentation, dictionary learning, PCA, OM

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