Unsupervised full-color cellular image reconstruction through disordered optical fiber

Wide-field color imaging through multimode fiber with single wavelength illumination: plug-and-play approach

Multimode fiber (MMF) is extensively studied for its ability to transmit light modes in parallel, potentially minimizing optical fiber size in imaging. However, current research predominantly focuses on grayscale imaging, with limited attention to color studies. Existing colorization methods often involve costly white light lasers or multiple light sources, increasing optical system expenses and space. To achieve wide-field color images with typical monochromatic illumination MMF imaging system, we proposed a data-driven "colorization" approach and a neural network called SpeckleColorNet, merging U-Net and conditional GAN (cGAN) architectures, trained by a combined loss function. This approach, demonstrated on a 2-meter MMF system with single-wavelength illumination and the Peripheral Blood Cell (PBC) dataset, outperforms grayscale imaging and alternative colorization methods in readability, definition, detail, and accuracy. Our method aims to integrate MMF into clinical medicine and industrial monitoring, offering cost-effective high-fidelity color imaging. It serves as a plug-and-play replacement for conventional grayscale algorithms in MMF systems, eliminating the need for additional hardware.

Feature decoupled knowledge distillation enabled lightweight image transmission through multimode fibers

Multimode fibers (MMF) show tremendous potential in transmitting high-capacity spatial information. However, the quality of multimode transmission is quite sensitive to inherent scattering characteristics of MMF and almost inevitable external perturbations. Previous research has shown that deep learning may break through this limitation, while deep neural networks are intricately designed with huge computational complexity. In this study, we propose a novel feature decoupled knowledge distillation (KD) framework for lightweight image transmission through MMF. In this framework, the frequency-principle-inspired feature decoupled module significantly improves image transmission quality and the lightweight student model can reach the performance of the sophisticated teacher model through KD. This work represents the first effort, to the best of our knowledge, that successfully applies a KD-based framework for image transmission through scattering media. Experimental results demonstrate that even with up to 93.4% reduction in model computational complexity, we can still achieve averaged Structure Similarity Index Measure (SSIM) of 0.76, 0.85, and 0.90 in Fashion-MNIST, EMNIST, and MNIST images respectively, which are very close to the performance of cumbersome teacher models. This work dramatically reduces the complexity of high-fidelity image transmission through MMF and holds broad prospects for applications in resource-constrained environments and hardware implementations.