Hundreds of images with the same polarization state are first registered to compensate for the jitters during an observation and then integrated to realize the needed spatial resolution and sensitivity for solar magnetic field measurement. Due to the feature dependent properties of the correlation tracker technique, an effective method to select the feature region is critical for low-resolution full-disk solar filtergrams, especially those with less significant features when the Sun is quiet. In this paper, we propose a region extraction method based on a Hessian matrix and information entropy constraints for local correlation tracking (CT) to get linear displacement between different images. The method is composed of three steps: (1) extract feature points with the Hessian matrix, (2) select good feature points with scale spaces and thresholds, and (3) locate the feature region with the twodimensional information entropy constraints. Both the simulated and observational experiments demonstrated that our region selection method can efficiently detect the linear displacement and improve the quality of a groundbased full-disk solar magnetogram. The local CT with the selected regions can obtain displacement detection results as good as the global CT and at the same time significantly reduce the average calculation time.

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