Multiplexing‐Enhanced Computational Imaging: High‐Fidelity Reconstruction and Color Imaging in Multimode Fibers

Abstract The advancement of computational imaging has propelled multimode fiber systems beyond conventional endoscopy by synergizing optical physics and deep learning to reconstruct images from disordered speckle patterns. However, existing multimode fiber (MMF) imaging predominantly relies on single‐channel transmission, limiting information capacity, imaging quality, and color fidelity. Here, multiplexing‐enhanced computational imaging is proposed to overcome these constraints through wavelength‐division multiplexing and multi‐channel fusion. By integrating inverse transmission matrix optimization, multi‐input Pix2Pix generative adversarial networks, and image fusion algorithms, the system reconstructs high‐fidelity color images from multiplexed speckle patterns encoded by RGB wavelengths in a single MMF. Experimental results demonstrate a structural similarity improvement from 0.649 to 0.799 and a 26.4% reduction in color deviation, achieving parallel data transmission and enhance imaging quality. A wavelength‐speckle relationship model and analyze multiplexing capacity limits are established, showing its extensibility to time‐, space‐, and polarization‐division multiplexing. This framework not only enables ultra‐thin full‐color micro‐endoscopy with superior information density, but also expands to hyperspectral imaging, optical neural networks, and quantum communication.