论文标题
一个简单的径向梯度过滤器,用于批处理冠状图像
A Simple Radial Gradient Filter for Batch-Processing of Coronagraph Images
论文作者
论文摘要
如白色灯光冠状动脉所观察到的,如白色灯光的图像所观察到的,如不同的白色冠状动脉所观察到的,包括K和F-Corona,并且强度的径向变化。这些图像需要将两个冠状成分分开,并进行一些额外的图像处理,以减少强度梯度,并分析在整个视野内太阳能电晕的不同高度上发生的结构和过程。为了用陡峭的径向强度梯度处理这些散装的冠状图像,我们开发了一种算法:简单的径向梯度滤波器(Sirgraf)。该算法基于减去使用长持续图像创建的最小背景(F-Corona),然后将结果除以均匀的强度梯度图像以增强k-corona。我们证明了这种算法的实用性,以提出电晕的短时间瞬态结构。 Sirgraf可用于揭示和分析此类结构。它不适用于基于强度的定量估计。我们已经成功地测试了太阳能和地球层观测站(SOHO)的LASCO-C2图像的算法,以及具有良好信号噪声比(SNR)的立体声板以及COR-2A以及COR-2A以及立体声/COR-1A的低SNR图像以及Kcoronagraph的低SNR图像。我们还比较了sirgraf的性能与归一化径向梯度滤波器(NRGF)。我们发现,当必须处理数百个图像时,Sirgraf的工作速度比NRGF更快,在图像中提供了相似的亮度和对比度,并分离了瞬态特征。此外,Sirgraf在COR-1A的低SNR图像上的工作效果要比NRGF更好,从而更好地识别整个视野的动态冠状结构。我们讨论算法的优点和局限性。
Images of the extended solar corona, as observed by white-light coronagraphs as observed by different white-light coronagraphs include the K- and F-corona and suffer from a radial variation in intensity. These images require separation of the two coronal components with some additional image-processing to reduce the intensity gradient and analyse the structures and processes occurring at different heights in the solar corona within the full field of view. To process these bulk coronagraph images with steep radial-intensity gradients, we have developed an algorithm: Simple Radial Gradient Filter (SiRGraF). This algorithm is based on subtracting a minimum background (F-corona) created using long-duration images and then dividing the resultant by a uniform intensity gradient image to enhance the K-corona. We demonstrate the utility of this algorithm to bring out the short time-scale transient structures of the corona. SiRGraF could be used to reveal and analyse such structures. It is not suitable for quantitative estimations based on intensity. We have successfully tested the algorithm on images of the LASCO-C2 onboard the Solar and Heliospheric Observatory (SOHO), and COR-2A onboard the STEREO with good signal to noise ratio (SNR) along with low-SNR images of STEREO/COR-1A and the KCoronagraph. We also compared the performance of SiRGraF with Normalising Radial Gradient Filter (NRGF). We found that when hundreds of images have to be processed, SiRGraF works faster than NRGF, providing similar brightness and contrast in the images and separating the transient features. Moreover, SiRGraF works better on low-SNR images of COR-1A than NRGF, providing better identification of dynamic coronal structures throughout the field of view. We discuss the advantages and limitations of the algorithm.