  Lensless fiber endomicroscope is an emerging tool for in-vivo microscopic
imaging, where quantitative phase imaging (QPI) can be utilized as a label-free
method to enhance image contrast. However, existing single-shot phase
reconstruction methods through lensless fiber endomicroscope typically perform
well on simple images but struggle with complex microscopic structures. Here,
we propose a speckle-conditioned diffusion model (SpecDiffusion), which
reconstructs phase images directly from speckles captured at the detection side
of a multi-core fiber (MCF). Unlike conventional neural networks, SpecDiffusion
employs iterative phase denoising steps for speckle-driven phase
reconstruction. The iteration scheme allows SpecDiffusion to break down the
phase reconstruction process into multiple steps, gradually building up to the
final phase image. This attribute alleviates the computation challenge at each
step and enables the reconstruction of rich details in complex microscopic
images. To validate its efficacy, we build an optical system to capture
speckles from MCF and construct a dataset consisting of 100,000 paired images.
SpecDiffusion provides high-fidelity phase reconstruction results and shows
powerful generalization capacity for unseen objects, such as test charts and
biological tissues, reducing the average mean absolute error of the
reconstructed tissue images by 7 times. Furthermore, the reconstructed tissue
images using SpecDiffusion shows higher accuracy in zero-shot cell segmentation
tasks compared to the conventional method, demonstrating the potential for
further cell morphology analysis through the learning-based lensless fiber
endomicroscope. SpecDiffusion offers a precise and generalized method to phase
reconstruction through scattering media, including MCFs, opening new
perspective in lensless fiber endomicroscopic imaging.
