Sunkit-Image: Final GSOC Report#

This project developed several image processing algorithms and manipulation routines for sunkit-image, an affiliated Python package of the Sunpy Project. The rigorous analysis of solar images is of paramount importance to the Heliophysics community as this can reveal more information on solar features and events, which in turn can affect the Earth. This project brought selected solar image processing algorithms under the umbrella of a new library.

GSOC 2019: Project Goals#

There were four major goals as listed on the OpenAstronomy website.
These included: * Implement the Normalizing Radial Gradient Filter * Port the Multi-scale Gaussian Normalization * Implement the OCCULT-2 algorithm for coronal loop tracing * Implement Soft Morphological filtering of the solar images

Project Goals Completed#

  • Implement the Normalizing Radial Gradient Filter Normalizing Radial Gradient Filter is an algorithm designed to enhance features off the solar limb in a solar image. It normalizes the radial gradient i.e., the sharp decrease in intensity values in images as the pixels increase in radial distance from the Sun’s centre which helps in visualizing the coronal structures. This has been completed and merged along with its sister algorithm the Fourier Normalizing Radial Gradient Filter. The code for this can be found on this PR.

  • Port the Multi-scale Gaussian Normalization Any solar image contains information distributed over a very wide range of spatial scales which can be mostly hidden due to the variation of intensity values in an image. Processing such an image to unveil that hidden information is therefore very important. Multi-scale Gaussian Normalisation effectively normalizes the pixel values locally at different scales by convolving with different widths of Gaussian kernels and can reduce noise locally revealing any hidden features. The algorithm was successfully implemented and the code has already been merged.

  • Implement the OCCULT-2 algorithm Oriented Coronal CUrved Loop Tracing (OCCULT) is an algorithm designed to automatically trace out coronal loops in an image. It traces out loops starting at the maximum flux position and then moving in a bidirectional fashion from that point. The code, documentation and examples are complete and can be found here, it is waiting for further reviews and will be merged shortly (< 2 weeks).

  • Implement the Soft Morphological filtering The soft morphological filter approach to removing cosmic ray hits in a solar image uses image morphology operators and genetic algorithms. However, a similar package called Astroscrappy that also removes cosmic ray hits was found. We found that the algorithm used by Astroscrappy had more citations as compared to the Soft Morphological filtering and in our tests, it produced good results on solar data. So it was decided that the Soft Morphological filtering will not be implemented and rather a detailed example on how to use Astroscrappy on solar data was written here.

Further Project Work#

These tasks were not part of the main GSoC project goals but were worked upon during the GSoC project.

  • Fourier Linear Correlation Tracking An existing C library was wrapped using Cython to enable Python calls to a Fourier Linear Correlation Tracking (FLCT) C library along with the tests and documentation for the wrapper. This algorithm aims at finding out the 2D flow field between two images. This work is complete and under review. However, as the C code is licenced under GPL v2 and sunkit-image is under BSD, we are waiting on permissions from the original authors before this can be merged or if it needs to be spun into a separate library.

  • Fourier Normalizing Radial Gradient Filter This was implemented as a run-up to GSoC and the tests and example were written during the coding period. This was merged along with the Normalizing Radial Gradient Filter.

  • Noise Level Estimation There was a preexisting noise estimation class in sunkit-image and it was decided that this was to be converted into a series of functions. Most of this task had already taken care of in PR 22, however, the original author was unable to finish this work and so this was completed in a new pull request and was merged.