计算机科学 ›› 2022, Vol. 49 ›› Issue (3): 185-191.doi: 10.11896/jsjkx.210100234
周颖1,2, 常明新1, 叶红1, 张燕1
ZHOU Ying1,2, CHANG Ming-xin1, YE Hong1, ZHANG Yan1
摘要： 针对太阳能电池板隐裂缺陷在进行光学检测时存在的特征不明显问题,以及小样本导致的训练不充分问题,提出了基于元迁移的太阳能电池板缺陷图像超分辨率重建方法,采用联合训练方法,利用内部图像和外部大规模图像信息分别作为不同阶段的训练数据。首先将引入的大量数据用于模型的初步训练,学习外部大规模数据的公共特征,然后通过元学习模型MAML进行多任务训练,为快速适应小样本无监督任务寻找一个适合图像内部学习的初始参数,提高模型的泛化能力,最后将预训练参数迁移至改进的ZSSR中进行自监督学习。在DIV2K、Set5、BSD100和太阳能电池板电致发光成像数据集上进行训练,实验结果表明,与传统的CARN,RCAN,IKC,ZSSR方法相比,该方法具有更高的峰值信噪比,最高达到36.66,参数量更小,相比ZSSR降低了70 000,图像重建时间更短,相比CARN降低了0.51 s,具有更好的重建效果,更高的重建效率。
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