# Multi-Temporal Frames Projection for Dynamic Processes Fusion in Fluorescence Microscopy

arXiv: https://arxiv.org/abs/2601.10392 · HTML: https://mlsanity.com/paper/2601.10392/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 50 references that are in the map.
- First posted on arXiv 15 Jan 2026.

## Builds on (most cited first, 6 of 6)

- [Similarity and Quality Metrics for MR Image-To-Image Translation](https://mlsanity.com/paper/2405.08431/) (2024)
- [Contrast Enhancement of Brightness-Distorted Images by Improved Adaptive Gamma Correction](https://mlsanity.com/paper/1709.04427/) (2017)
- [3D Vascular Segmentation Supervised by 2D Annotation of Maximum Intensity Projection](https://mlsanity.com/paper/2402.12128/) (2024)
- [Monocular Depth Decomposition of Semi-Transparent Volume Renderings](https://mlsanity.com/paper/2206.13282/) (2022)
- [Segmenting Cardiac Muscle Z-disks with Deep Neural Networks](https://mlsanity.com/paper/2401.13472/) (2024)
- [Medical X-Ray Image Enhancement Using Global Contrast-Limited Adaptive Histogram Equalization](https://mlsanity.com/paper/2411.01373/) (2024)

## Abstract (arXiv)

Fluorescence microscopy is widely employed for the analysis of living biological samples; however, the utility of the resulting recordings is frequently constrained by noise, temporal variability, and inconsistent visualisation of signals that oscillate over time. We present a unique computational framework that integrates information from multiple time-resolved frames into a single high-quality image, while preserving the underlying biological content of the original video. We evaluate the proposed method through an extensive number of configurations (n = 111) and on a challenging dataset comprising dynamic, heterogeneous, and morphologically complex 2D monolayers of cardiac cells. Results show that our framework, which consists of a combination of explainable techniques from different computer vision application fields, is capable of generating composite images that preserve and enhance the quality and information of individual microscopy frames, yielding 44% average increase in cell count compared to previous methods. The proposed pipeline is applicable to other imaging domains that require the fusion of multi-temporal image stacks into high-quality 2D images, thereby facilitating annotation and downstream segmentation.