Joint Dictionary Learning for Example-based Image Super-resolution
This work addresses image quality enhancement for applications like photography or medical imaging, but it appears incremental as it builds on existing example-based super-resolution methods.
The paper tackles image super-resolution by proposing a joint dictionary learning method that trains low-resolution and high-resolution dictionaries using sparse representation, ensuring consistent sparse representations across resolutions while minimizing reconstruction error. Simulation results demonstrate its effectiveness compared to state-of-the-art algorithms.
In this paper, we propose a new joint dictionary learning method for example-based image super-resolution (SR), using sparse representation. The low-resolution (LR) dictionary is trained from a set of LR sample image patches. Using the sparse representation coefficients of these LR patches over the LR dictionary, the high-resolution (HR) dictionary is trained by minimizing the reconstruction error of HR sample patches. The error criterion used here is the mean square error. In this way we guarantee that the HR patches have the same sparse representation over HR dictionary as the LR patches over the LR dictionary, and at the same time, these sparse representations can well reconstruct the HR patches. Simulation results show the effectiveness of our method compared to the state-of-art SR algorithms.