# Robust Registration of Calcium Images by Learned Contrast Synthesis

arXiv: https://arxiv.org/abs/1511.01154 · HTML: https://mlsanity.com/paper/1511.01154/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2015.

## Built on by (most cited first, 5 of 5)

- [Is Image-to-Image Translation the Panacea for Multimodal Image Registration? A Comparative Study](https://mlsanity.com/paper/2103.16262/) (2021)
- [Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy](https://mlsanity.com/paper/2104.09435/) (2021)
- [Multi-Marginal Flow Matching with Adversarially Learnt Interpolants](https://mlsanity.com/paper/2510.01159/) (2025)
- [Guided-deconvolution for Correlative Light and Electron Microscopy](https://mlsanity.com/paper/2208.09451/) (2022)
- [Evaluating Transformation Models for pCLE Mosaic Registration](https://mlsanity.com/paper/2609.24560/) (2026)

## Abstract (arXiv)

Multi-modal image registration is a challenging task that is vital to fuse complementary signals for subsequent analyses. Despite much research into cost functions addressing this challenge, there exist cases in which these are ineffective. In this work, we show that (1) this is true for the registration of in-vivo Drosophila brain volumes visualizing genetically encoded calcium indicators to an nc82 atlas and (2) that machine learning based contrast synthesis can yield improvements. More specifically, the number of subjects for which the registration outright failed was greatly reduced (from 40% to 15%) by using a synthesized image.