DefocusTrackerAI -- A Generalized Framework for the Automatic Detection of Defocused Particle Images

• URL: http://arxiv.org/abs/2606.00076v1
• Date: Fri, 22 May 2026 08:01:22 GMT
• Title: DefocusTrackerAI -- A Generalized Framework for the Automatic Detection of Defocused Particle Images
• Abstract summary: DefocusTrackerAI is a framework for the automatic detection and position estimation of defocused particle images.<n>YOLOv9 is trained on a diverse and feature-rich synthetic image set containing astigmatic and non-astigmatic defocused particle images of varying diameters.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The present work introduces DefocusTrackerAI, a generalized deep-learning framework for the automatic detection and position estimation of defocused particle images from any kind of optical configuration without compromising uncertainty and recall, intended as a follow-up of the open-source project DefocusTracker. We selected the deep neural network architecture from the direct comparison of two well-known object detection models, Faster R-CNN and YOLOv9, trained on a diverse and feature-rich synthetic image set containing astigmatic and non-astigmatic defocused particle images of varying diameters. The model evaluation on synthetic data showed that, first, YOLOv9 outperforms Faster R-CNN, achieving higher recall and lower uncertainty, particularly at high particle image densities; and second, that YOLOv9 provides enhanced spatial resolution, with uncertainty values between 0.1 and 0.4 pixels for particle image densities N_s up to 0.5, outperforming state-of-the-art algorithms. We demonstrated that our models are able to detect astigmatic and non-astigmatic defocused particle images in multiple optical setups with varying lighting conditions. In addition, we successfully applied our models on real DPT experiments, including fluorescence and shadowgraph data, showing that they can be used beyond conventional DPT applications, including the tracking of sprays and droplets. A pre-trained, ready-to-use version of DefocusTrackerAI based on YOLOv9 is available at https://gitlab.com/goncalo.coutinho/defocustrackerAI-main/-/tree/7e0f11f649ebad50e20dca5b9545f26ca30 3ebe0 and can be used for automatic detection of defocused particle images of any kind with high accuracy. In combination with a suitable calibration approach for the depth position, it can be used as an effective first step for three-dimensional defocusing particle tracking.

Related papers

• Realistic Compound-Lens Defocus Blur Synthesis [16.115267588640453]
  We propose a pipeline for synthesizing realistic defocus deblurring datasets for diverse compound lenses.<n>We generate CLDefocus, a large-scale synthetic dataset containing lens-diverse defocus image pairs.
  arXiv  Detail & Related papers  (2026-07-07T05:00:44Z)
• Defocus Aberration Theory Confirms Gaussian Model in Most Imaging Devices [0.0]
  This paper introduces the settings, for conventional imaging devices, to ensure that the defocusing operator adheres to the Gaussian model.<n>The results demonstrate a maximum Mean Absolute Error $(!M!A!E)$ of less than $1%$, underscoring the model's accuracy and reliability.
  arXiv  Detail & Related papers  (2026-01-08T10:03:13Z)
• Fine-grained Defocus Blur Control for Generative Image Models [66.30016220484394]
  Current text-to-image diffusion models excel at generating diverse, high-quality images.<n>We introduce a novel text-to-image diffusion framework that leverages camera metadata.<n>Our model enables superior fine-grained control without altering the depicted scene.
  arXiv  Detail & Related papers  (2025-10-07T17:59:15Z)
• DeepAf: One-Shot Spatiospectral Auto-Focus Model for Digital Pathology [37.648157065553185]
  We introduce a novel automated microscopic system powered by DeepAf.<n>DeepAf combines spatial and spectral features through a hybrid architecture for single-shot focus prediction.<n>System achieves 0.90 AUC in cancer classification at 4x magnification, a significant achievement at lower magnification than typical 20x WSI scans.
  arXiv  Detail & Related papers  (2025-10-06T19:28:08Z)
• Efficient Depth- and Spatially-Varying Image Simulation for Defocus Deblur [16.9629875455607]
  Deep learning models trained on existing open-source datasets often face domain gaps and do not perform well in real-world settings.<n>We propose an efficient and scalable dataset approach that does not rely on fine-tuning with real-world data.<n>Our method simultaneously models depth-dependent defocus and spatially varying optical aberrations, addressing both computational complexity and the scarcity of high-quality RGB-D datasets.
  arXiv  Detail & Related papers  (2025-07-01T02:03:04Z)
• Examining the Impact of Optical Aberrations to Image Classification and Object Detection Models [58.98742597810023]
  Vision models have to behave in a robust way to disturbances such as noise or blur.<n>This paper studies two datasets of blur corruptions, which we denote OpticsBench and LensCorruptions.<n> Evaluations for image classification and object detection on ImageNet and MSCOCO show that for a variety of different pre-trained models, the performance on OpticsBench and LensCorruptions varies significantly.
  arXiv  Detail & Related papers  (2025-04-25T17:23:47Z)
• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models.<n>In this paper, we investigate how detection performance varies across model backbones, types, and datasets.<n>We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• Deep Dynamic Scene Deblurring from Optical Flow [53.625999196063574]
  Deblurring can provide visually more pleasant pictures and make photography more convenient. It is difficult to model the non-uniform blur mathematically. We develop a convolutional neural network (CNN) to restore the sharp images from the deblurred features.
  arXiv  Detail & Related papers  (2023-01-18T06:37:21Z)
• Decentralized Autofocusing System with Hierarchical Agents [2.7716102039510564]
  We propose a hierarchical multi-agent deep reinforcement learning approach for intelligently controlling the camera and the lens focusing settings. The algorithm relies on the latent representation of the camera's stream and, thus, it is the first method to allow a completely no-reference tuning of the camera.
  arXiv  Detail & Related papers  (2021-08-29T13:45:15Z)
• Single image deep defocus estimation and its applications [82.93345261434943]
  We train a deep neural network to classify image patches into one of the 20 levels of blurriness. The trained model is used to determine the patch blurriness which is then refined by applying an iterative weighted guided filter. The result is a defocus map that carries the information of the degree of blurriness for each pixel.
  arXiv  Detail & Related papers  (2021-07-30T06:18:16Z)
• Universal and Flexible Optical Aberration Correction Using Deep-Prior Based Deconvolution [51.274657266928315]
  We propose a PSF aware plug-and-play deep network, which takes the aberrant image and PSF map as input and produces the latent high quality version via incorporating lens-specific deep priors. Specifically, we pre-train a base model from a set of diverse lenses and then adapt it to a given lens by quickly refining the parameters.
  arXiv  Detail & Related papers  (2021-04-07T12:00:38Z)
• Deep Autofocus for Synthetic Aperture Sonar [28.306713374371814]
  In this letter, we demonstrate the potential of machine learning, specifically deep learning, to address the autofocus problem. We formulate the problem as a self-supervised, phase error estimation task using a deep network we call Deep Autofocus. Our results demonstrate Deep Autofocus can produce imagery that is perceptually as good as benchmark iterative techniques but at a substantially lower computational cost.
  arXiv  Detail & Related papers  (2020-10-29T15:31:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.