Full-disk solar images are provided by many solar telescopes around the world. However, the ob- served images show Non-Radial Variation (NRV) over the disk. In this paper, we propose algorithms for detecting distortions and restoring these images. For detecting NRV, the cross-correlation coefficients matrix of radial profiles is calculated and the minimum value in the matrix is defined as the Index of Non-radial Variation (INV). This index has been utilized to evaluate the H images of GONG, and systemic variations of different instruments are obtained. For obtaining the NRV’s image, a Multi-level Morphological Filter (MMF) is designed to eliminate structures produced by solar activities over the solar surface. Comparing with the median filter, the proposed filter is a better choice. The experimental results show that the effect of our automatic detection and restoration methods is significant for getting a flat and high contrast full-disk image. For investigating the effect of our method on solar features, structural similarity (SSIM) index is utilized. The high SSIM indices (close to 1) of solar features show that the details of the structures remain after NRV restoring.

Inter-granular Bright Points (igBPs) are small-scale objects in the Solar photosphere which can be seen within dark inter-granular lanes. We present a new algorithm to automatically detect and extract igBPs. Laplacian and Morphological Dilation (LMD) technique is employed by the algorithm. It involves three basic processing steps: (1) obtaining candidate "seed" regions by Laplacian; (2) determining the boundary and size of igBPs by morphological dilation; (3) discarding brighter granules by a probability criterion. For validating our algorithm, we used the observed samples of the Dutch Open Telescope (DOT), collected on April 12, 2007. They contain 180 high-resolution images, and each has a 85×68arcsec2 85×68arcsec2 field of view (FOV). Two important results are obtained: first, the identified rate of igBPs reaches 95% and is higher than previous results; second, the diameter distribution is 220±25km 220±25km , which is fully consistent with previously published data. We conclude that the presented algorithm can detect and extract igBPs automatically and effectively.