Morphological-Based Filtering of Noise: Practical Study on Solar Images

In this paper, a morphological-based algorithm is proposed for noise filtering in digital images. This algorithm is based on the morphological hit-miss transform (HMT). It is applied on a real-life problem, which is the detection of solar features in H-alpha solar images that are obtained from Meudon Observatory. These images are processed by the automated detection system of Filaments reported by R. Qahwaji and T. Colak [1]. The automated detection system works well when detecting filaments in noise-free solar images; it achieves false acceptance rate (FAR) error rate of 4% and false rejection rate (FRR) error rate of 36% when compared with the manually detected filaments in the synoptic maps. When the detection is applied after the addition of Gaussian noise to the solar images it achieves FAR of 3% and FRR of 51%. Then by filtering using the proposed algorithm, the detection performance is enhanced to achieve FAR of 8% and FRR of 13%.