Attention-based neural network for polarimetric image denoising

被引：17
|
作者：
Liu, Hedong [1 ]
Zhang, Yizhu [1 ]
Cheng, Zhenzhou [1 ]
Zhai, Jingsheng [2 ]
Hu, Haofeng [1 ,2 ]
机构：
[1] Tianjin Univ, Sch Precis Instrument & Optoelect Engn, Tianjin 300072, Peoples R China
[2] Tianjin Univ, Sch Marine Sci & Technol, Tianjin 300072, Peoples R China
来源：
OPTICS LETTERS | 2022年 / 47卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Image denoising;
D O I：
10.1364/OL.458514
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
In this Letter, we propose an attention-based neural network specially designed for the challenging task of polarimetric image denoising. In particular, the channel attention mechanism is used to effectively extract the features underlying the polarimetric images by rescaling the contributions of channels in the network. In addition, we also design the adaptive polarization loss to make the network focus on the polarization information. Experiments show that our method can well restore the details flooded by serious noise and outperforms previous methods. Moreover, the underlying mechanism of channel attention is revealed visually. (C) 2022 Optica Publishing Group
引用
收藏
页码：2726 / 2729
页数：4
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