基于生成对抗网络的梯度引导太阳斑点图像去模糊方法

针对云南天文台拍摄的高度模糊的太阳斑点图像采用现有深度学习算法恢复难度大、高频信息难以重建等问题，提出了一种基于生成对抗网络（GAN）与梯度信息联合的去模糊方法来重建太阳斑点图，并很好地恢复出图像的高频信息。该方法由一个生成器与两个鉴别器构成：首先，生成器采用特征金字塔网络（FPN）框架来获取图像多尺度特征，再将这些特征分层次输入梯度分支以梯度图的形式捕获更小的局部特征；然后，联合梯度分支结果与FPN结果共同重建出具有高频信息的太阳斑点图像；其次，在常规对抗鉴别器的基础上，增加了一个鉴别器用于保证由梯度分支产生的梯度图更加真实；最后，引入一个包括像素内容损失、感知损失和对抗损失的联合训练损失来引导模型进行太阳斑点图像高分辨率重建。实验结果表明，进行图像预处理后的所提方法与现有的深度学习去模糊方法相比，高频信息恢复能力更强，峰值信噪比（PSNR）和结构相似性（SSIM）指标均有显著提高，分别达到27.801 0 dB与0.851 0，能够满足太阳观测图像高分辨率重建的需要。

Solar speckle image deblurring method with gradient guidance based on generative adversarial network

With the existing deep learning algorithms， it is difficult to restore the highly blurred solar speckle images taken by Yunnan Observatories， and it is difficult to reconstruct the high-frequency information of images. In order to solve the problems， a deblurring method for restoring the solar speckle images and recovering the high-frequency information of images based on Generative Adversarial Network （GAN） and gradient information was proposed. The proposed method was consisted of one generator and two discriminators. Firstly， the image multi-scale features were obtained by the generator with the Feature Pyramid Network （FPN） framework， and these features were input into the gradient branch hierarchically to capture the smaller details in the form of gradient map， and the solar speckle image with high-frequency information was reconstructed by combining the gradient branch results and the FPN results. Then， based on the conventional adversarial discriminator， another discriminator was added to ensure the gradient map generated by the gradient branch more realistic. Finally， a joint training loss including pixel content loss， perceptual loss and adversarial loss was introduced to guide the model to perform high-resolution reconstruction of solar speckle images. Experimental results show that， compared with the existing deep learning deblurring method， the proposed method with image preprocessing has stronger ability to recover the high-frequency information， and significantly improves the Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity （SSIM） indicators， reaching 27.801 0 dB and 0.851 0 respectively. The proposed method can meet the needs for high-resolution reconstruction of solar observation images.