Automated detecting and removing cloud shadows in full-disk solar images

Abstract

Sky clouds affect full-disk solar observations significantly. Their shadows obscure the details of solar features in observed images. Cloud-covered images are difficult to be used for further research without pre-processing. We proposed a technique for detecting and removing cloud shadows in full-disk solar images. In the detection procedure, a two-step approach is applied: (1) identifying the deviation of a cloud-covered image from a perfect disk; (2) quantifying the cloud cover by an index that we defined in this paper. In the removal procedure, the transmittance of clouds is measured by comparing the cloud-covered image with a “Quiet Sun” that getting from a normal observation. A restored cloud-free image can be obtained after correcting the absorption by clouds. We tested the procedures using the full-disk solar Hα images of the Global Oscillation Network Group (GONG), and utilized the structural similarity (SSIM) algorithm for evaluating the performance of image restoration. The results demonstrate that both procedures are significant effective, and that the cloud-covered image is restored not only in visual effect but also in intensities of solar features.