AI4Life Microscopy Supervised Denoising Challenge 2025¶
Why Denoising Matters in Microscopy¶
Microscopy images are crucial for scientific research, particularly in biology and medicine. However, raw microscopy images often suffer from noise introduced during image acquisition, making analysis difficult and potentially obscuring critical details. Removing this noise from microscopy images is essential to improve image quality while preserving key features like edges, textures, and fine details.
In recent years, deep learning-based denoising has emerged as a successful approach to removing noise while retaining useful signals. Unlike classical algorithms, which apply predefined filters, deep learning models learn from data, providing more intelligent, content-aware noise reduction. These methods have already improved image analysis for many researchers, significantly enhancing downstream tasks such as segmentation, classification, and quantitative analysis.
Aim of the challenge¶
Following our AI4Life Denoising Challenge 2024, which focused on unsupervised denoising, this year’s challenge shifts to supervised denoising - leveraging high-quality training data to achieve more precise and reliable noise removal.
Supervised denoising is an approach where a model is trained using paired noisy and clean images. By learning the relationship between noisy inputs and their corresponding noise-free versions, the model can accurately remove noise while preserving important details. This method ensures more reliable and high-quality image restoration compared to unsupervised approaches, which lack explicit ground truth references. Researchers can directly evaluate model performance against a known ground truth, ensuring better interpretability and control.
This challenge will evaluate both established methods and novel approaches, with the goal of pushing the boundaries of image restoration in scientific microscopy. By participating, you’ll contribute to a critical area of biomedical research, help improve microscopy image processing, and potentially enable new scientific discoveries.
About this challenge¶
This challenge is organized by the AI4Life Open Calls and Open Challenges team. We aim to showcase bioimage analysis problems to a broader audience of computational experts and work together in friendly competition, jointly pushing the boundaries of what automated solutions can do for a given analysis task.
About us¶
AI4Life is a Horizon Europe-funded project that brings together the computational and life science communities.
Its goal is to empower life science researchers to harness the full potential of Artificial Intelligence (AI) and Machine Learning (ML) methods for bioimage analysis, particularly microscopy image analysis, by providing services, and developing standards aimed at both developers and users.
AI4Life promises to create harmonized and interoperable AI tools & methods via open calls and public challenges and bring these developments to researchers via strategic outreach and advanced training.
The services provided and solutions developed within the AI4Life framework are crucial to solving today’s microscopy image analysis problems and will contribute to boosting the pace of biological and medical insights and discovery in the coming years.
The BioImage Model Zoo and FAIR data principles are core facets of the AI4Life project.
AI4Life has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101057970. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.