抚仙湖太阳观测和研究基地知识库

太阳斑点图具有对比度较低、米粒结构相似、帧间差异较小的特点，现有重建网络在进行单帧去模糊时存在高频特征不足、局部细节难以恢复等问题。结合图像多聚焦融合，构建梯度增强与FPN双阶段网络实现太阳斑点图的高分辨率重建。首先，利用序列图像帧间相似信息互补特性，使用块聚焦图像融合算法，弥补图像丢失的高频细节；其次，以生成对抗网络GAN为框架，设计了一个双阶段重建网络DSGEF,联合梯度分支与结构特征分支增强高频细节，再利用FPN网络进行多尺度特征重建，改善米粒边缘清晰度；最后，引入一个包含对抗损失、像素损失和感知损失的联合损失函数，用于引导网络DSGEF进行训练，实现高分辨率太阳斑点图的重建。实验结果表明，该方法与现有深度学习方法相比，峰值信噪比（PSNR）和结构相似性（SSIM）指标均有明显提高，能够满足太阳观测图像高分辨率重建要求。 

Because the solar speckle image has the characteristics of low constrast, similar structure of rice grains and small difference between frames, there are some problems such as insufficient high-frequency features and unrecoverable local details when using the existing reconstruction network for single frame deblurring. In this paper, a high-resolution reconstruction method of solar speckle image is proposed by combining multi-focus fusion and building gradient enhancement and FPN two-stage network. Firstl, the block-focused image fusion algorithm is performed to compensate for high-frequency details lost in the images by utilizing the complementary characteristics of similar information between sequence images. Secondly, a two-stage reconstruction network DSGEF is constructed based on the generative adversarial network (GAN), which combines gradient branches and structural feature branches to enhance high-frequency details, uses FPN network for multi-scale featrue reconstruction to improve the definition of rice grain edges. Finally, a joint training loss including adversarial loss, pixel loss and perceptual loss is introduced to guide the network to implement high-resolution reconstruction of solar speckle images. Experimental results show that, compared with existing deep learning methods, the proposed method can significantly improve the image peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) indicators, and can meet the requrements of high-resolution reconstruction of solar observation images.