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

TL;DR

The paper introduces SiRGraF, a simple and fast radial gradient filter algorithm for processing coronagraph images, enhancing transient coronal structures while being efficient and effective on low-SNR data.

Contribution

A novel radial gradient filter algorithm, SiRGraF, designed for quick processing and enhanced detection of transient coronal features in large coronagraph image datasets.

Findings

SiRGraF effectively enhances transient structures in coronagraph images.

It outperforms NRGF on low-SNR images in speed and quality.

The algorithm is suitable for bulk processing of large datasets.

Abstract

Images of the extended solar corona, as observed by white-light coronagraphs 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. To process these bulk coronagraph images with steep radial-intensity gradients, we have developed an 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 could 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 LASCO-C2 onboard the Solar and Heliospheric Observatory (SOHO), and COR-2A onboard the STEREO with good signal to noise ratio (SNR) along with low-SNR images of STEREO/COR-1A and the KCoronagraph. We also compared the performance of SiRGraF with Normalising 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 NRGF, providing better identification of dynamic coronal structures throughout the field of view. We discuss the advantages and limitations of the algorithm.