3-D Reconstruction
- Software: The 'scraytrace' package (under the SECCHI distribution in IDL SolarSoft). Developer: Dr. A. Thernisien (NRL)
- Reconstruction based on forward modeling. User choses a shape to represent the target object (e.g. ellipsoid for a shock, Graduate cylindrical Shell for a CME, slab for a streamer, etc.) and visually fits it in 2 or 3 near-simultaneous images.
- Works for EUV and white light images.
![](images/3DReconstruction-sm.png)
3D reconstructions of three overlapping CMEs using 3-viewpoint observations from the SECCHI and LASCO coronagraphs. (from Colaninno & Vourlidas 2015; Credit: NRL/NASA).
Wavelet-Enhanced Images
- Developer: Dr. G. Stenborg (NRL). See Stenborg et al. (2008) for details or contact Dr. Stenborg.
- The full STEREO EUVI archive in 4 wavelengths (17.1, 19.5, 28.4, 30.4 nm) is available online.
- Monthly 2-color movies (17.1/19.5, 28.4/30.4, 171.4/30.4) at 2-hr cadence are available here.
- The algorithm works equally well for SDO/AIA images. Examples are available in our gallery.
![](images/Wavelet-Enhanced.png)
Example of STEREO-A EUVI '2-color' composites combining EUV images at 30.4 nm (red) and 19.5 nm (blue). Left: images processed with standard Solarsoft EUVI calibration. Right: Images further enhanced with wavelet decomposition. (Courtesy: Dr. G. Stenborg, NRL).
Supervised Image Processing
- Developed for tracking CMEs in LASCO and SECCHI/COR2 by N. Goussies & G. Stenborg (Goussies et al. 2010).
- Used in the Multi-Viewpoint COR2 (MVC) Catalog to extract CME properties.
![](images/Sup-Image-Process.png)
Example of tracking the CME envelope (grey contours), position angle (solid line) and direction (stars) in successive images via an automated algorithm. (Credit: L. Balmaceda & NRL/NASA).
Automated Filament Detection
- Developed by P. Bernasconi to detect and track filaments on the solar disk and extract parameters such as chirality using Hα images (Bernasconi et al. 2005).
- The Advanced Automated Filament Detection and Characterization Code (AAFDCC) is part of the SDO Computer Vision project and has been running since 2010 (see Martens et al. 2012, for details)
![](images/Auto-Filament-Detect.png)
Automated filament identification (from Martens et al. 2012. Credit: Elsevier)