A Two-stage Raman Imaging Denoising Algorithm Based on Deep Learning

Quan Tang1; Jiaqi Hu1; Jinna Chen1; Chenlong Xue1; Junfan Chen1; Hong Dang1; Dan Lu1; Huanhuan Liu1; Qizhen Sun2; Qiaozhou Xiong3; Longqing Cong1; Perry Ping Shum1

10.1109/ACP55869.2022.10088904

2022

Asia Communications and Photonics Conference (ACP) / International Conference on Information Photonics and Optical Communications (IPOC)

2162-108X

978-1-6654-8156-4

2022 Asia Communications and Photonics Conference (ACP)

2096-2099

5-8 Nov. 2022

Shenzhen, China

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

For the problem of low Signal-to-Noise Ratio (SNR) of the image reconstructed from Raman spectra, this paper proposes a two-stage denoising algorithm based on deep learning, including spectrum denoising and image denoising. Because spectra and images of the same sample are scarce, the spectrum denoising algorithm and the image denoising one are trained on two irrelevant dataset. Denoising and baseline correction are performed on the raw Raman spectra using 1D-ResUNet. The low SNR images are denoised by Reversed Convolutional Block Attention Module UNet (RCBAM-UNet). Experimental results showed that the Mean Squared Error (MSE) between the denoised spectrum processed by our method and the ground truth decreased by 10(3)-10(4), when compared to the raw data. The quantitative results of image denoising on Structural Similarity (SSIM) and Peak Signal-to-Noise Ratio (PSNR) are 98.4% and 39.7, respectively. The performance of our two-stage algorithm on the testing dataset (an independent dataset) showed increased SNR and Contrast to Noise Ratio (CNR) in Raman imaging denoising. Our study provides an efficient method for the improvement of Raman image quality.

Raman imaging deep learning Raman spectrum denoising image denoising

第一

英语

CPCI-S

National Natural Science Foundation of China[62220106006]

Engineering ; Optics ; Telecommunications

Engineering, Electrical & Electronic ; Optics ; Telecommunications

WOS:001000552100542

IEEE

1.Department of Electrical and Electronic Engineering, College of Engineering, Southern University of Science and Technology, Shenzhen, China
2.School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
3.Innermedical Co, Shenzhen, China

Quan Tang,Jiaqi Hu,Jinna Chen,et al. A Two-stage Raman Imaging Denoising Algorithm Based on Deep Learning[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2022:2096-2099.