计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 101-106.doi: 10.11896/jsjkx.200600144
王建明1, 黎向锋1, 叶磊1, 左敦稳1, 张丽萍2
WANG Jian-ming1, LI Xiang-feng1, YE Lei1, ZUO Dun-wen1, ZHANG Li-ping2
摘要： 清晰的医学图像可以有效地帮助医生进行病理分析和病情诊断。针对医学图像中的显微图像在采集过程中因失焦产生的图像模糊问题,文中以生成对抗网络去模糊模型DeblurGAN作为基本框架,提出了一种新的图像去模糊网络。该网络在生成器结构中引入信道注意结构(Channel Attention,CA),有效地提取了图像的细节特征。图像上采样过程中使用双线性插值+卷积的结构代替反卷积(转置卷积)过程,消除了棋盘效果。使用对抗损失、内容损失相结合的方式训练模型来获得清晰的图像。实验结果表明,该网络较DeblurGAN生成的去模糊图像,在PSNR和SSIM指标上都获得了较大的提升。
中图分类号:
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