针对现有红外图像分辨率低、质量不高的问题,提出了基于通道注意力与迁移学习的红外图像超分辨率重建方法。该方法设计了一个深度卷积神经网络,融入通道注意力机制来增强网络的学习能力,并且使用残差学习方式来减轻梯度爆炸或消失问题,加速网络的收敛。考虑到高质量的红外图像难以采集、数目不足的情况,将网络的训练分成两步:第一步使用自然图像来预训练网络模型,第二步利用迁移学习的知识,用较少数量的高质量红外图像对预训练的模型参数进行迁移微调,使模型对红外图像的重建效果更优。最后,加入多尺度细节滤波器来提升红外重建图像的视觉效果。在Set5、Set14数据集以及红外图像上的实验表明,融入通道注意力机制和残差学习方法,均能提升超分辨率重建的效果,迁移微调能很好地解决红外样本数量不足的问题,而多尺度细节提升滤波则能提升重建图像的细节,增大信息量。
基于通道注意力与迁移学习的红外图像超分辨率重建算法
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出版日期:2021年1月15日
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参考文献
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基金项目:
国家自然科学基金面上项目(61471154,61876057);中央高校基本科研业务费专项资金资助项目(JZ2018YYPY0287)
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孙锐, 章晗, 程志康, 等. 基于通道注意力与迁移学习的红外图像超分辨率重建算法[J]. 光电工程, 2021, 48(1): 200045.
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