Multiscale AM-FM Demodulation and Image Reconstruction Methods With Improved Accuracy

By Víctor Murray, Paul A. Rodríguez, Marios S. Pattichis. 2010

93 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Constantinos S. Pattichis
2. Marios S. Pattichis
3. Víctor Murray
4. Andreas Holzinger

In Computational Mechanics

1. Constantinos S. Pattichis
2. Marios S. Pattichis
3. Víctor Murray
4. Andreas Holzinger

In Computer Vision and Pattern Recognition

1. Constantinos S. Pattichis
2. Chris Biemann
3. Douglas B. Kell
4. Andreas Holzinger
5. Víctor Murray
6. Marios S. Pattichis
7. E. Barriga
8. Peter Soliz

Citations in the corpus

• What do we need to build explainable AI systems for the medical domain? (2017, methodology): “The introduction of a multiscale approach in (Murray et al., 2010) demonstrated that the method can be used to reconstruct general images.”
• An adaptive multiscale AM-FM texture analysis system with application to hysteroscopy imaging (2012, methodology): “By using AM-FM components from different scales, we can produce multiscale features at pixel-level resolutions [5].”
• Tree Image Growth Analysis Using Instantaneous Phase Modulation (2011, unknown): “Tree Image Growth Analysis Using Instantaneous”
• Recent multiscale AM-FM methods in emerging applications in medical imaging (2012, methodology): “Since the AM-FM estimates of neighboring pixels could be affected by noisy estimates, a 9 × 9 median filter is often applied to the output of the IA and IF estimates (see [4,5,14]).”