# HistoSpeckle-Net: Mutual Information-Guided Deep Learning for high-fidelity reconstruction of complex OrganAMNIST images via perturbed Multimode Fibers

arXiv: https://arxiv.org/abs/2511.20245 · HTML: https://mlsanity.com/paper/2511.20245/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 25 Nov 2025.

## Builds on (most cited first, 9 of 9)

- [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://mlsanity.com/paper/2110.14795/) (2021)
- [HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms](https://mlsanity.com/paper/2011.11731/) (2020)
- [Deep learning the high variability and randomness inside multimode fibres](https://mlsanity.com/paper/1807.09351/) (2018)
- [Transmission of natural scene images through a multimode fibre](https://mlsanity.com/paper/1904.11985/) (2019)
- [Image computing for fibre-bundle endomicroscopy: A review](https://mlsanity.com/paper/1809.00604/) (2018)
- [Image reconstruction through a multimode fiber with a simple neural network architecture](https://mlsanity.com/paper/2006.05708/) (2020)
- [Image transmission through a flexible multimode fiber by deep learning](https://mlsanity.com/paper/2011.05144/) (2020)
- [Robust real-time imaging through flexible multimode fibers](https://mlsanity.com/paper/2210.13883/) (2022)
- [Towards optimal multimode fiber imaging by leveraging input polarization and deep learning](https://mlsanity.com/paper/2310.17889/) (2023)

## Abstract (arXiv)

Existing deep learning methods in multimode fiber (MMF) imaging often focus on simpler datasets, limiting their applicability to complex, real-world imaging tasks. These models are typically data-intensive, a challenge that becomes more pronounced when dealing with diverse and complex images. In this work, we propose HistoSpeckle-Net, a deep learning architecture designed to reconstruct structurally rich medical images from MMF speckles. To build a clinically relevant dataset, we develop an optical setup that couples laser light through a spatial light modulator (SLM) into an MMF, capturing output speckle patterns corresponding to input OrganAMNIST images. Unlike previous MMF imaging approaches, which have not considered the underlying statistics of speckles and reconstructed images, we introduce a distribution-aware learning strategy. We employ a histogram-based mutual information loss to enhance model robustness and reduce reliance on large datasets. Our model includes a histogram computation unit that estimates smooth marginal and joint histograms for calculating mutual information loss. It also incorporates a unique Three-Scale Feature Refinement Module, which leads to multiscale Structural Similarity Index Measure (SSIM) loss computation. Together, these two loss functions enhance both the structural fidelity and statistical alignment of the reconstructed images. Our experiments on the complex OrganAMNIST dataset demonstrate that HistoSpeckle-Net achieves higher fidelity than baseline models such as U-Net and Pix2Pix. It gives superior performance even with limited training samples and across varying fiber bending conditions. By effectively reconstructing complex anatomical features with reduced data and under fiber perturbations, HistoSpeckle-Net brings MMF imaging closer to practical deployment in real-world clinical environments.