四川轻化工大学人工智能四川省重点实验室;国网四川省电力公司电力科学研究院;
为了使图像修复方法在结构重建的过程中实现结构与纹理信息之间的交互，提高修复的图像在语义上的真实性。在原有的双流生成网络基础上改进了一种基于BIFPN多尺度特征融合算法的双流结构图像修复网络。该网络采用耦合方式实现结构约束下的纹理合成与纹理引导下的结构重建，实现纹理与结构信息的有效利用，有利于生成语义更真实的图像。构建BIFPN多尺度特征融合网络，以实现重建、感知与风格损失的补偿，使融合后的图像实现全局的一致性。在训练阶段，采用了基于语义的联合损失函数，以增强图像在结构生成上的合理性。通过在CelebA和Places2数据集上与其他修复网络进行对比实验，证明了改进的图像修复方法的客观评价指标更优，更加有效地修复破损图像的结构和纹理信息，图像修复性能更优。
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基本信息:
DOI：
中图分类号:TP391.41
引用信息:
[1]李兰,陈明举,石浩德等.基于BIFPN-GAN特征融合的图像修复算法研究[J].无线电工程,2022,52(12):2141-2148.
基金信息:
企业信息化与物联网测控技术四川省高校重点实验室开放基金资助(2021WYY01); 人工智能四川省重点实验室项目(2020RZY02); 四川轻化工大学研究生创新基金项目(y2021078)~~