NVST Image Denoising Based on Self-Supervised Deep Learning

As one of the most important optical solar telescopes for studying solar physics, the new vacuum solar telescope (NVST) suffers from various noise interferences in the process of collecting image data, which affects the study of observation data. Some methods based on standard supervised deep learning have achieved remarkable results in the field of image denoising. However, this type of method becomes inapplicable to the field of astronomical images where clean data is difficult to obtain. To solve this problem, this paper applies the image denoising method based on self-supervised deep learning to NVST image denoising. In order to quantitatively evaluate the performance of the network model, first, add simulation noise to the reconstructed data; second, the noisy data is estimated through the noise level estimation network to estimate the noise level; then, self-supervised convolution blind spot network is used to learn image features, and the image is restored by Baycsian inference; finally, the experimental results arc quantitatively analyzed through the peak signal-to-noise ratio (PSNR) and structural similarity evaluation indicators, correlation analysis, and power spectrum analysis. Experimental results show that, whether for simulated noise data or actual observation data, compared with other image denoising methods in the experiment, the proposed method can effectively reduce the interference of noise on NVST images and improve the image PSNR. At the same time, it also provides solutions for other engineering fields where clean image data is difficult to obtain.