# Deep Joint Denoising and Detection for Enhanced Intracellular Particle Analysis

arXiv: https://arxiv.org/abs/2408.07903 · HTML: https://mlsanity.com/paper/2408.07903/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 59 references that are in the map.
- First posted on arXiv 15 Aug 2024.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [Noise2Noise: Learning Image Restoration without Clean Data](https://mlsanity.com/paper/1803.04189/) (2018)
- [Learning Deconvolution Network for Semantic Segmentation](https://mlsanity.com/paper/1505.04366/) (2015)
- [Noise2Void - Learning Denoising from Single Noisy Images](https://mlsanity.com/paper/1811.10980/) (2018)
- [A survey of loss functions for semantic segmentation](https://mlsanity.com/paper/2006.14822/) (2020)
- [Scaled-YOLOv4: Scaling Cross Stage Partial Network](https://mlsanity.com/paper/2011.08036/) (2020)
- [Deep Learning on Image Denoising: An overview](https://mlsanity.com/paper/1912.13171/) (2019)
- [Numerical Coordinate Regression with Convolutional Neural Networks](https://mlsanity.com/paper/1801.07372/) (2018)
- [Connecting Image Denoising and High-Level Vision Tasks via Deep Learning](https://mlsanity.com/paper/1809.01826/) (2018)
- [Simultaneous Denoising and Localization Network for Photoacoustic Target Localization](https://mlsanity.com/paper/2104.14713/) (2021)

## Abstract (arXiv)

Reliable analysis of intracellular dynamic processes in time-lapse fluorescence microscopy images requires complete and accurate tracking of all small particles in all time frames of the image sequences. A fundamental first step towards this goal is particle detection. Given the small size of the particles, their detection is greatly affected by image noise. Recent studies have shown that applying image denoising as a preprocessing step indeed improves particle detection and their subsequent tracking. Deep learning based particle detection methods have shown superior results compared to traditional detection methods. However, they do not explicitly aim to remove noise from the images to facilitate detection. Thus we hypothesize that their performance could be further improved. In this paper, we propose a new deep neural network, called DENODET (denoising-detection network), which performs image denoising and particle detection simultaneously. We show that integrative denoising and detection yields more accurate detection results. Our method achieves superior results compared to state-of-the-art particle detection methods on the particle tracking challenge dataset and our own real fluorescence microscopy image data.