Abstract
A new Bayesian Super-Resolution (SR) image registration and reconstruction method is proposed. The new method utilizes a prior distribution based on a general combination of spatially adaptive, or non-stationary, image filters, which includes an adaptive local strength parameter able to preserve both image edges and textures. With the application of variational techniques, the proposed method allows for the automatic estimation of all problem unknowns. An experimental comparison between state of the art methods and the proposed SR approach has been performed on both synthetic and real images.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1-10 |
| Number of pages | 10 |
| Journal | Digital Signal Processing: A Review Journal |
| Volume | 32 |
| DOIs | |
| State | Published - Jan 1 2014 |
Keywords
- Bayesian methods
- Parameter estimation
- Super-resolution
- Total variation
- Variational methods
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering
Fingerprint Dive into the research topics of 'A non-stationary image prior combination in super-resolution'. Together they form a unique fingerprint.
Cite this
Villena, S., Vega, M., Molina, R., & Katsaggelos, A. K. (2014). A non-stationary image prior combination in super-resolution. Digital Signal Processing: A Review Journal, 32, 1-10. https://doi.org/10.1016/j.dsp.2014.05.017