计算机科学 ›› 2023, Vol. 50 ›› Issue (11A): 230100091-8.doi: 10.11896/jsjkx.230100091
李玥玥1, 刘万平1, 黄东2
LI Yueyue1, LIU Wanping1, HUANG Dong2
摘要: 由于GPU计算的快速发展,深度学习近年来在图像降噪方面得到了应用。大多数深度学习方法都需要无噪声图像作为训练标签,但通常它们很难获得,甚至不可能获得。于是,有学者开始研究使用噪声图像进行降噪网络训练,但其恢复的图像却面临丢失细节信息的问题。受Noise2Noise(N2N)的思想启发,文中使用成对的噪声图像训练神经网络,学习同一范围的同类型噪声之间的分布关系,实现了一种新的降噪网络模型。新开发的模型(MA-UNet)基于经典UNet架构,融合了多头注意力机制(Multi-head Attention)和简易的残差网络,可以更好地挖掘图像的关键信息,掌握特征的全局信息,从而恢复更清晰的图像。与传统算法(CBM3D)和其他方法(如DnCNN和B2U)相比,MA-UNet的性能参数优良。从视觉图像观察来看,所提模型恢复了更清晰的图像细节。与N2N设计的模型相比,在不同噪声幅值下,所提模型在4个经典数据集上的峰值信噪比和结构相似性指数的均值均有显著提高。
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