# Single-sample image-fusion upsampling of fluorescence lifetime images

arXiv: https://arxiv.org/abs/2404.13102 · HTML: https://mlsanity.com/paper/2404.13102/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 84 references that are in the map.
- First posted on arXiv 19 Apr 2024.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Accelerating the Super-Resolution Convolutional Neural Network](https://mlsanity.com/paper/1608.00367/) (2016)
- [Deep Learning for Image Super-resolution: A Survey](https://mlsanity.com/paper/1902.06068/) (2019)
- [DiffuserCam: Lensless Single-exposure 3D Imaging](https://mlsanity.com/paper/1710.02134/) (2017)
- [Light Stage Super-Resolution: Continuous High-Frequency Relighting](https://mlsanity.com/paper/2010.08888/) (2020)
- [Machine learning for faster and smarter fluorescence lifetime imaging microscopy](https://mlsanity.com/paper/2008.02320/) (2020)
- [Giga-voxel multidimensional fluorescence imaging combining single-pixel detection and data fusion](https://mlsanity.com/paper/2107.12337/) (2021)
- [Super-Resolution Time-Resolved Imaging using Computational Sensor Fusion](https://mlsanity.com/paper/2101.03949/) (2021)

## Built on by (most cited first, 1 of 1)

- [Multimodal Super-Resolution: Discovering hidden physics and its application to fusion plasmas](https://mlsanity.com/paper/2405.05908/) (2024)

## Abstract (arXiv)

Fluorescence lifetime imaging microscopy (FLIM) provides detailed information about molecular interactions and biological processes. A major bottleneck for FLIM is image resolution at high acquisition speeds, due to the engineering and signal-processing limitations of time-resolved imaging technology. Here we present single-sample image-fusion upsampling (SiSIFUS), a data-fusion approach to computational FLIM super-resolution that combines measurements from a low-resolution time-resolved detector (that measures photon arrival time) and a high-resolution camera (that measures intensity only). To solve this otherwise ill-posed inverse retrieval problem, we introduce statistically informed priors that encode local and global dependencies between the two single-sample measurements. This bypasses the risk of out-of-distribution hallucination as in traditional data-driven approaches and delivers enhanced images compared for example to standard bilinear interpolation. The general approach laid out by SiSIFUS can be applied to other image super-resolution problems where two different datasets are available.