# Large model enhanced computational ghost imaging

arXiv: https://arxiv.org/abs/2503.08710 · HTML: https://mlsanity.com/paper/2503.08710/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 47 references that are in the map.
- First posted on arXiv 10 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [EnlightenGAN: Deep Light Enhancement without Paired Supervision](https://mlsanity.com/paper/1906.06972/) (2019)
- [Computational ghost imaging using deep learning](https://mlsanity.com/paper/1710.08343/) (2017)
- [Compressive adaptive computational ghost imaging](https://mlsanity.com/paper/1304.0243/) (2013)
- [Turbulence-immune computational ghost imaging based on a multi-scale generative adversarial network](https://mlsanity.com/paper/2107.07870/) (2021)

## Built on by (most cited first, 4 of 4)

- [Single-Pixel Vision-Language Model for Intrinsic Privacy-Preserving Behavioral Intelligence](https://mlsanity.com/paper/2601.17050/) (2026)
- [Mitigating Long-Tail Bias in HOI Detection via Adaptive Diversity Cache](https://mlsanity.com/paper/2511.18811/) (2025)
- [Compressive sensing inspired self-supervised single-pixel imaging](https://mlsanity.com/paper/2603.29732/) (2026)
- [Two-Stage Teacher-Student Reliable Prior Learning for Robust Underwater Image Enhancement](https://mlsanity.com/paper/2608.00137/) (2026)

## Abstract (arXiv)

Ghost imaging (GI) achieves 2D image reconstruction through high-order correlation of 1D bucket signals and 2D light field information, particularly demonstrating enhanced detection sensitivity and high-quality image reconstruction via efficient photon collection in scattering media. Recent investigations have established that deep learning (DL) can substantially enhance the ghost imaging reconstruction quality. Furthermore, with the emergence of large models like SDXL, GPT-4, etc., the constraints of conventional DL in parameters and architecture have been transcended, enabling models to comprehensively explore relationships among all distinct positions within feature sequences. This paradigm shift has significantly advanced the capability of DL in restoring severely degraded and low-resolution imagery, making it particularly advantageous for noise-robust image reconstruction in GI applications. In this paper, we propose the first large imaging model with 1.4 billion parameters that incorporates the physical principles of GI (GILM). The proposed GILM implements a skip connection mechanism to mitigate gradient explosion challenges inherent in deep architectures, ensuring sufficient parametric capacity to capture intricate correlations among object single-pixel measurements. Moreover, GILM leverages multi-head attention mechanism to learn spatial dependencies across pixel points during image reconstruction, facilitating the extraction of comprehensive object information for subsequent reconstruction. We validated the effectiveness of GILM through a series of experiments, including simulated object imaging, imaging objects in free space, and imaging object located 52 meters away in underwater environment. The experimental results show that GILM effectively analyzes the fluctuation trends of the collected signals, thereby optimizing the recovery of the object's image from the acquired data.