A Simple Radial Gradient Filter for Batch-Processing of Coronagraph Images

Images of the extended solar corona, as observed by different white-light coronagraphs, include the K- and F-corona and suffer from a radial variation in intensity. These images require separation of the two coronal components with some additional image-processing to reduce the intensity gradient and analyse the structures and processes occurring at different heights in the solar corona within the full field of view. Over the past few decades, coronagraphs have been producing enormous amounts of data, which will be continued with the launch of new telescopes. To process these bulk coronagraph images with steep radial-intensity gradients, we have developed the algorithm Simple Radial Gradient Filter (SiRGraF). This algorithm is based on subtracting a minimum background (F-corona) created using long-duration images and then dividing the resultant by a uniform-intensity-gradient image to enhance the K-corona. We demonstrate the utility of this algorithm to bring out the short-time-scale transient structures of the corona. SiRGraF can be used to reveal and analyse such structures. It is not suitable for quantitative estimations based on intensity. We have successfully tested the algorithm on images of the Large Angle Spectroscopic COronagraph (LASCO)-C2 onboard the Solar and Heliospheric Observatory (SOHO) and COR-2A onboard the Solar TErrestrial RElations Observatory (STEREO) with good signal-to-noise ratio (SNR) along with low-SNR images of STEREO/COR-1A and the KCoronagraph (KCor). We also compared the performance of SiRGraF with the existing widely used algorithm Normalizing Radial Gradient Filter (NRGF). We found that when hundreds of images have to be processed, SiRGraF works faster than NRGF, providing similar brightness and contrast in the images and separating the transient features. Moreover, SiRGraF works better on low-SNR images of COR-1A than on NRGF, providing better identification of dynamic coronal structures throughout the field of view. We discuss the advantages and limitations of the algorithm. The application of SiRGraF to COR-1 images can be extended for an automated coronal mass ejection (CME) detection algorithm in the future, which will help in our study of the characteristics of CMEs in the inner corona.

Automated summary

This article introduces an algorithm called SiRGraF for processing solar corona images, which is capable of reducing intensity gradients, separating corona structures, and revealing transient structures at short time-scales. SiRGraF performs well on low signal-to-noise ratio images and works faster than the existing algorithm NRGF with similar brightness and contrast.