# Image Algorithm Summary

This table lists the image reconstruction algorithms available in the RHESSI software. Click the Name of the algorithm for more detail on that method.

Algorithm | Description | Advantages | Disadvantages |

Back Projection | Most basic method of image reconstruction. Multiplies the calibrated event list by the collimator modulation patterns to construct a 'dirty map' of the image. | Very fast. Linear. Simple. | Poor quality images with sidelobes. No reliable source sizes. |

Clean | Iteratively identifies peaks in the back projection map and removes contributions from the associated sources convolved with the Point Spread Function. The reconstructed image is the sum of Gaussians based on the point source fluxes. | Relatively fast. Positive fluxes. Removes sidelobes. | Prone to photometric errors. Likely to misinterpret extended sources. |

Forward Fit | Assumes the source(s) can be presented as a simple functional form with a few free parameters (for example, a circular Gaussian source) and looks for the parameters that produce a map that is consistent with the data. | Fast. Positive fluxes. Provides source sizes and errors on parameters. | Requires that assumptions concerning the number of sources and their shapes are accurate. |

Pixon | Seeks a superposition of circular sources or pixons of different sizes and parabolic profiles that best reproduces the measured modulations from the different detectors. The goal is to construct the image with the fewest degrees of freedom (the fewest pixons) that is consistent with the observations. | Robust. Accurate image photometry. Best method to image extended sources in the presence of compact sources. | Slow. Subjective smoothing required to avoid source breakup at high resolution. |

MEM NJIT | Maximum Entropy Method algorithm based on visibilities. This is a 2-D version of radio astronomy's SSMEM provided by NJIT. | Fast. Accurate image photometry and morphology. Capable of super-resolution. | Subject to source breakup when using data from the finest grids. |

VIS FWDFIT | Forward-fit algorithm based on visibilities. Like the Forward-Fit algorithm, assumes there are a limited number of simple individual sources that can be parameterized. Adjusts the source parameters such that the model-predicted visibilities agree best with the measured visibilities. | Fast. Provides uncertainties on source parameters. | Requires that assumptions concerning the number of sources and their shapes are accurate. Currently limited to one or two circular or elliptical Gaussians, or a single loop. |

UV SMOOTH | Performs a Fast Fourier Transform of 2-D spline-interpolated visibilities. | Fast. Good photometry. | Vulnerable to missing visibilities. |

EM | An iterative reconstruction method based on the Lucy-Richardson Maximum Likelihood method. At each step, uses a back-projection of the ratio of observed counts to the expected counts to modify current image. Does not use the FLATFIELD back-projection and modifies image by multiplying by a positive image therefore rigorously maintains positivity in the image. | Poor-man's version of Pixon. Speed is a few times faster than Clean and 10-20 times faster than Pixon. Similar photometric qualities to Pixon. Fairly robust to choice of detectors. | Does not explore solution space as thoroughly as Pixon. Images will break up if pushed too hard. Avoid using EM with very high # of counts (>1e6) as there is no allowance for systematic errors in the counts. |

| Maximum Entropy Methods (MEM) algorithm finds the 'most plausible' solution which has the maximum configuration entropy among those solutions that are consistent with the observation. | Relatively fast. Positive fluxes. | Unsupported. |

| Similar to MEM Sato, except works with visibilities instead of counts. | Relatively fast. Positive fluxes. | Unsupported. |