Abstract

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.

© 2022 Optica Publishing Group

Learning-based denoising for polarimetric images

Xiaobo Li, Haiyu Li, Yang Lin, Jianhua Guo, Jingyu Yang, Huanjing Yue, Kun Li, Chuan Li, Zhenzhou Cheng, Haofeng Hu, and Tiegen Liu
Opt. Express 28(11) 16309-16321 (2020)

Pol2Pol: self-supervised polarimetric image denoising

Hedong Liu, Xiaobo Li, Zhenzhou Cheng, Tiegen Liu, Jingsheng Zhai, and Haofeng Hu
Opt. Lett. 48(18) 4821-4824 (2023)

Physics-informed neural network for polarimetric underwater imaging

Haofeng Hu, Yilin Han, Xiaobo Li, Liubing Jiang, Li Che, Tiegen Liu, and Jingsheng Zhai
Opt. Express 30(13) 22512-22522 (2022)

High-definition image transmission through dynamically perturbed multimode fiber by a self-attention based neural network

Guohua Wu, Yong Sun, Longfei Yin, Zhixiong Song, and Wenting Yu
Opt. Lett. 48(10) 2764-2767 (2023)

TIPFNet: a transformer-based infrared polarization image fusion network

Kunyuan Li, Meibin Qi, Shuo Zhuang, Yanfang Yang, and Jun Gao
Opt. Lett. 47(16) 4255-4258 (2022)