Resolution-invariant coding for continuous image super-resolution

The paper presents the resolution-invariant image representation (ЯIIRЯIIR) framework. It applies sparse-coding with multi-resolution codebook to learn resolution-invariant sparse representations of local patches. An input image can be reconstructed to higher resolution at not only discrete integer scales, as that in many existing super-resolution works, but also continuous scales, which functions similar to 2-D image interpolation. The ЯIIRЯIIR framework includes the methods of building a multi-resolution bases set from training images, learning the optimal sparse resolution-invariant representation of an image, and reconstructing the missing high-frequency information at continuous resolution level. Both theoretical and experimental validations of the resolution invariance property are presented in the paper. Objective comparison and subjective evaluation show that the ЯIIRЯIIR framework based image resolution enhancement method outperforms existing methods in various aspects.