This invention discloses a hardware-
software co-
processing method for real-time spectral
signal processing in Raman
microscopy, belonging to the field of spectral
signal processing technology. The method includes: establishing strict temporal constraints between physical displacement and cloud
inference, and initiating
asynchronous communication using the physical gaps in stage movement; simultaneously, deploying a
deep learning model (F2P) pre-trained with a self-supervised masking strategy in the cloud. This pre-trained model utilizes the
structural correlation between the Raman
broadband background and
narrowband characteristic peaks to directly perform real-time forward
inference on the input single-frame noisy spectrum, achieving
millisecond-level high-fidelity reconstruction of a
single frame without any complex parameter tuning or the need for a clean control spectrum. This invention completely eliminates the physical
lag in multi-frame processing and the general technical bias of
supervised learning algorithms, achieving true "synchronization between acquisition and display," and significantly improving the real-
time processing performance and cross-device automated deployment capabilities of in-situ
Raman imaging.