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Abstract Solar images observed in different channels with different instruments are crucial to the study of solar activity. However, the images have different fields of view, causing them to be misaligned. It is essential to accurately register the images for studying solar activity from multiple perspectives. Image registration is described as an optimizing problem from an image to be registered to a reference image. In this paper, we proposed a novel coarse-to-fine solar image registration method to register the multichannel solar images. In the coarse registration step, we used the regular step gradient descent algorithm as an optimizer to maximize the normalized cross correlation metric. The fine registration step uses the Powell–Brent algorithms as an optimizer and brings the Mattes mutual information similarity metric to the minimum. We selected five pairs of images with different resolutions, rotation angles, and shifts to compare and evaluate our results to those obtained by scale-invariant feature transform and phase correlation. The images are observed by the 1.6 m Goode Solar Telescope at Big Bear Solar Observatory and the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Furthermore, we used the mutual information and registration time criteria to quantify the registration results. The results prove that the proposed method not only reaches better registration precision but also has better robustness. Meanwhile, we want to highlight that the method can also work well for the time-series solar image registration. 

Abstract Multiwavelength bright points (BPs) are taken to be cross sections of magnetic flux tubes extending from the surface of the photosphere upward to the higher photosphere. We aim to study the characteristics of isolated multiwavelength BPs using the cotemporal and cospatial TiO band and H α line wings from the Goode Solar Telescope at Big Bear Solar Observatory. A deep-learning method, based on Track Region-based Convolutional Neural Networks, is proposed to detect, segment, and match the BPs across multiple wavelength observations, including the TiO, H α + 1 Å, H α − 1 Å, H α + 0.8 Å, and H α − 0.8 Å line wings. Based on the efficient detection and matching result with a precision of 0.98, 1283 groups of BPs matched in all five wavelengths are selected for statistics analysis. The characteristic values of the BPs observed at the same red and blue line wings are averaged. For the BPs of the TiO, averaged H α ± 1 Å, and averaged H α ± 0.8 Å line wings, the mean equivalent diameters are 162 ± 32, 254 ± 33, and 284 ± 28 km, respectively. The maximum intensity contrasts are 1.11 ± 0.09, 1.05 ± 0.03, and 1.05 ± 0.02 , respectively. The mean eccentricities are 0.65 ± 0.14, 0.63 ± 0.11, and 0.65 ± 0.11, respectively. Moreover, the characteristic ratios of each H α ± 1 Å and H α ± 0.8 Å BP to its corresponding TiO BP are derived. H α ± 1 Å and H α ± 0.8 Å line wings BPs show 60% and 80% increases compared to TiO BPs, respectively. With increasing height, most BPs almost keep their shapes. This work is helpful for modeling the three-dimensional structure of flux tubes.