Single color image super-resolution using quaternion-based sparse representation

In current color image super-resolution methods, superresolution based on sparse representation achieves state-of-the-art performance. However, the exploited sparse representation models deal with the color images as independent channel planes. Consequently, these approaches process the color pixels as scalar quantity, lacking of accuracy in describing inter-relationship among color channels. In this paper, we propose a quaternion-based online dictionary learning method and solve color image super-resolution by employing a quaternion-based sparse representation model. This sparse representation model implements color image superresolution 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.