Automatic detection of coronal loops using guided filtering and wavelet transform

Abstract

The corona is the outer layer of the solar asmosphere. The bright loop-like structures in the corona, which are called coronal loops, are hot plasma to be bound to the solar coronal magnetic fields. Studying these magnetic features can help to better understand the dynamics of coronal magnetic fields and coronal oscillations, and further clearify the debate of coronal heating. Accurate identifying coronal loops are crucial for the relevant studies. However, due to the complexity of the coronal magnetic fields, it is very difficult to identify and extract the loop-like structures. In this case, we propose a novel algorithm based on guided filtering and wavelet transform modulus maxima to automatically detect and extract them. The steps are as follows: we use (1) a fuzzy function to enhance the contrast of solar corona images; (2) guided filtering to highlight the edges of coronal loops; (3) wavelet transform modulus maxima to identitify the edges of coronal loops; (4) image binarization to extract coronal loops; (5) morphological operate to remove those non-loop structures. The images observed by the Transition and Coronal Explorer (TRACE) and the Atmospheric Imaging Assembly on the Solar Dynamics (SDO/AIA) intruments in the 171 A channel are selected to illustrate the porcess and further evaluate the performance of the algorithm. The results demonstrate that the proposed algorithm has significant advantages over provious identification methods and the detected and extracted structures can be further applied to scientific researchs.