Raman spectral preprocessing strategy based on self-supervised learning (RSPSSL) and its spectral preprocessing performance. (IMAGE)
Caption
a The general pipeline of the RSPSSL scheme includes spectral training datasets, an RSGAN model, and an RSBPCNN model. b Visualization of a selected raw spectrum (Navy blue) and its corresponding ideal spectrum (Baby blue). The roseate curve subtribes the raw and ideal spectrum, reflecting the spectral fidelity.The preprocessing model RSBPCNN# has excellent noise removal and baseline correction capabilities, and the processed spectral fidelity is high. This ability to extract weak signals with different signal-to-noise ratios reduces sampling time and improves downstream applications.
Credit
by Jiaqi Hu, Gina Jinna Chen, Chenlong Xue, Pei Liang, Yanqun Xiang, Chuanlun Zhang, Xiaokeng Chi, Guoying Liu, Yanfang Ye, Dongyu Cui, De Zhang, Xiaojun yu, Hong Dang, Wen Zhang, Junfan Chen, Quan Tang, Penglai Guo, Ho-Pui Ho, Yuchao Li, Longqing Cong & Perry Ping Shum
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CC BY