Real‐Time Multicolor Fluorescence Microscopy via Cross‐Channel Acquisition and Deep‐Learning‐Based Inference

Monitoring real‐time interactions in microrobotic and biological systems necessitates rapid multicolor fluorescence microscopy to resolve cellular, micro‐agent, and environmental dynamics. Sequential acquisition is widely used to capture multiple fluorescence channels while minimizing spectral crosstalk and photobleaching. However, this approach limits imaging speed in proportion to the number of channels, reducing its suitability for capturing micro‐agents and cellular interactions. This study introduces a real‐time multicolor reconstruction framework that exploits cross‐channel inputs (frames containing mixed spectral contributions) to enable simultaneous reconstruction of target fluorescence channels. The framework is evaluated on a custom‐built three‐channel fluorescence microscope, benchmarking two representative models: a standard supervised convolutional encoder–decoder with skip connections (U‐Net) and an adversarially trained conditional model (pix2pix). Experimental validation is conducted in a microfluidic environment containing active functionalized structures (Coumarin 6‐labeled electrospun magnetic fibers) and passive biological agents (CellTracker Red CMTPX‐labeled HeLa cell spheroids), where external magnetic fields actuate the micro‐agents to drive interactions with the spheroids. Prediction performance is evaluated in two‐ and three‐channel settings, yielding high‐fidelity reconstructions at 13.9 ms inference time. The proposed approach can increase the effective frame rate for three‐color channels by up to 83%, enabling high‐throughput multicolor imaging.