A Center-Median Filtering Method for Detection of Temporal Variation in Coronal Images (1511.04481v2)

Abstract: Events in the solar corona are often widely separated in their timescales, which can allow them to be identified when they would otherwise be confused with emission from other sources in the corona. Methods for cleanly separating such events based on their timescales are thus desirable for research in the field. This paper develops a technique for identifying time-varying signals in solar coronal image sequences which is based on a per-pixel running median filter and an understanding of photon-counting statistics. Example applications to 'EIT Waves' and small-scale dynamics are shown, both using data from the 193 Angstrom channel on AIA. The technique is found to discriminate EIT Waves more cleanly than the running and base difference techniques most commonly used. It is also demonstrated that there is more signal in the data than is commonly appreciated, finding that the waves can be traced to the edge of the AIA field of view when the data are rebinned to increase the signal-to-noise ratio.

• The paper introduces the Running Center-Median (RCM) filter, a new technique using a running median to detect temporal variations and enhance signal-to-noise ratio in solar coronal images.
• The RCM filter compares current pixel intensity to a temporal median, effectively detecting subtle phenomena such as EIT waves more reliably than standard techniques.
• This filtering method offers improved separation of transient solar events from background emissions, with potential for further refinement and application across different wavelengths.

Overview of the Center-Median Filtering Method for Coronal Images

The paper under consideration presents a new technique for identifying time-varying signals within sequences of solar coronal images, known as the Running Center-Median (RCM) filter. The method operates on individual pixels' time series using a running median-based filtering approach, which is applied across a specified time interval. This research is situated at the intersection of solar physics and image processing, aiming to address challenges in discerning dynamic phenomena in the solar corona, particularly those obscured by noise or other simultaneous events.

Methodology and Numerical Results

The central methodology involves replacing the per-frame approach used in conventional "running difference" methods with a median-based calculation that maintains sensitivity to dynamic changes while mitigating the influence of monotonic trends. Specifically, the RCM filter compares the current image intensity at each pixel with the median intensity within a defined temporal window centered on that pixel, highlighting deviations that suggest dynamic activity. The choice of a median, as opposed to a mean, helps reduce the influence of systematic trends and maintains focus on non-monotonic variations indicative of solar activity like EIT waves.

Numerical results reveal the RCM filter's capacity to detect subtle, large-scale solar phenomena, such as EIT waves, more effectively than traditional methods. The paper describes how, via temporal and spatial aggregation (rebinning), the technique significantly enhances the signal-to-noise ratio (SNR), allowing for detection of faint waves across the entire AIA field of view. Such capability was not as pronounced with standard techniques like base or running differences, which often struggle with distinguishing these faint signals due to underlying trends.

Implications and Future Directions

From a practical perspective, this proposed filtering approach offers a cleaner separation of transient solar events from background emissions, a critical advantage for solar physicists focusing on solar dynamics investigation. The utilization of Poisson statistics for calculating statistical significance of detected variations is valuable as it leverages the known noise characteristics of instruments used, such as the Atmospheric Imaging Assembly (AIA). This efficiency in detecting significant changes without substantial susceptibility to background shifts addresses a critical limitation in existing image processing strategies used in solar studies.

Theoretically, the methodology opens avenues for further exploration of temporal dynamics in the solar corona, with potential applications stretching across different wavelengths and imaging technologies. This contribution underscores the need for continued investigation into image filtering techniques that maintain high sensitivity to temporal change without undue sensitivity to background variation.

Future work may explore refining the method to improve SNR further and incorporate spatial multi-scale decompositions. This could enhance the detectability of global-scale transient events while sustaining necessary spatial resolution. Attention may also be directed towards overcoming the limitations tied to noise and bimodal distributions that introduce artifacts in the processed images.

In conclusion, the RCM filter presents a promising advancement in solar image analysis, offering a method that could refine how solar phenomena are detected and understood, contributing substantively to the ongoing research in heliophysical studies. This foundation suggests fertile ground for further methodological development and empirical validation in real-world solar observation contexts.