计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 38-42.doi: 10.11896/jsjkx.201000160
崔雯昊1, 蒋慕蓉1, 杨磊2, 傅鹏铭1, 朱凌霄1
CUI Wen-hao1, JIANG Mu-rong1, YANG Lei2, FU Peng-ming1, ZHU Ling-xiao1
摘要： 太阳斑点图高分辨率重建是天文图像处理的重要研究内容之一。基于深度学习的图像高分辨率重建,通过神经网络模型学习获得低分辨率图像到高分辨率图像的端到端映射函数,能够恢复图像高频信息,但在用于特征单一、噪音较多、局部细节模糊的太阳斑点图重建时,存在边缘过于平滑、高频信息易丢失等不足。将输入图像与重建图像结构特征加入CycleGAN网络中得到MCycleGAN,利用生成器网络从结构特征中获取高频信息,计算特征差来增强网络重建高频信息的能力;将残差块和融合层加入DeepFuse网络中构建RFCNN,利用图像帧间相似信息互补进行多帧重建,使重建图像边缘更加清晰。利用所提方法的重建结果与云南天文台使用的斑点掩膜法Level1+的结果对比表明,所提算法具有误差小、重建图像清晰度高等优点。
中图分类号:
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