Joel Sjöberg, Nicoleta Siminea, Andrei Păun, Adrian Lita, Mioara Larion, Ion Petre
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引用次数: 0
摘要
拉曼光谱[j] .文献10.1002/adom。202500736, Ion Petre及其同事介绍了RADAR,这是两种轻量级深度学习模型,可同时对拉曼光谱进行降噪和校正,在保持信号完整性的同时将数据采集时间缩短了90%。通过简化工件去除,RADAR提高了拉曼光谱在材料科学,生物医学研究等各种应用中的速度,准确性和可用性。
RADAR: Raman Spectral Analysis Using Deep Learning for Artifact Removal (Advanced Optical Materials 25/2025)
Raman Spectroscopy
In article 10.1002/adom.202500736, Ion Petre and co-workers introduce RADAR, two lightweight deep learning models that simultaneously denoise and correct Raman spectra, reducing data acquisition time by up to 90% while preserving signal integrity. By streamlining artifact removal, RADAR enhances the speed, accuracy, and usability of Raman spectroscopy across diverse applications in materials science, biomedical research, and beyond.
期刊介绍:
Advanced Optical Materials, part of the esteemed Advanced portfolio, is a unique materials science journal concentrating on all facets of light-matter interactions. For over a decade, it has been the preferred optical materials journal for significant discoveries in photonics, plasmonics, metamaterials, and more. The Advanced portfolio from Wiley is a collection of globally respected, high-impact journals that disseminate the best science from established and emerging researchers, aiding them in fulfilling their mission and amplifying the reach of their scientific discoveries.