Novel Super-Resolution Method Based on High Order Nonlocal-Means
This work addresses super-resolution for image reconstruction, but it appears incremental as it builds on existing NLM methods.
The authors tackled the problem of super-resolution reconstruction with general motion by generalizing the Non-Local Means method to higher orders using kernel regression, resulting in a performance comparison with other methods.
Super-resolution without explicit sub-pixel motion estimation is a very active subject of image reconstruction containing general motion. The Non-Local Means (NLM) method is a simple image reconstruction method without explicit motion estimation. In this paper we generalize NLM method to higher orders using kernel regression can apply to super-resolution reconstruction. The performance of the generalized method is compared with other methods.