结合多聚焦融合和DSGEF双阶段网络重建太阳斑点图

Combining Multi-focus Fusion and DSGEF Two-stage Network to Reconstruct Solar Speckle Image

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 contrast,similar structure of rice grains and small diffe-rence 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.Firstly,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 feature 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 requirements of high-resolution reconstruction of solar observation images.