Solar Image Denoising Using Non-Local Means Method

Solar physics study relies on the high-resolution imaging observation. So, solar observed images are important to study solar physics. However, the observed solar images are usually disturbed by image noise, such as Gauss and salt-pepper noise. The denoise methods, such as image smooth, wavelet transforms, machine learning are used to remove the image noise. But the methods have usually some limitations. Therefore, we proposed a novel method using a non-local mean algorithm to remove solar image noise for improving solar image qualities. We revealed and evaluated our proposed method using the observation data taken by the Goode Solar Telescope (GST) at the Big Bear Solar Observatory (BBSO). The results prove that our proposed method is accurate and correct with respect to the solar image denoising.