Recent Advancements in Microscopy Image Enhancement using Deep Learning: A Survey
- URL: http://arxiv.org/abs/2509.15363v2
- Date: Fri, 26 Sep 2025 06:24:21 GMT
- Title: Recent Advancements in Microscopy Image Enhancement using Deep Learning: A Survey
- Authors: Debasish Dutta, Neeharika Sonowal, Risheraj Barauh, Deepjyoti Chetia, Sanjib Kr Kalita,
- Abstract summary: There has been a significant rise in the advancement of microscopy image enhancement, specifically with the help of deep learning methods.<n>This survey paper aims to provide a snapshot of this rapidly growing state-of-the-art method, focusing on its evolution, applications, challenges, and future directions.<n>The core discussions take place around the key domains of microscopy image enhancement of super-resolution, reconstruction, and denoising, with each domain explored in terms of its current trends and their practical utility of deep learning.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Microscopy image enhancement plays a pivotal role in understanding the details of biological cells and materials at microscopic scales. In recent years, there has been a significant rise in the advancement of microscopy image enhancement, specifically with the help of deep learning methods. This survey paper aims to provide a snapshot of this rapidly growing state-of-the-art method, focusing on its evolution, applications, challenges, and future directions. The core discussions take place around the key domains of microscopy image enhancement of super-resolution, reconstruction, and denoising, with each domain explored in terms of its current trends and their practical utility of deep learning.
Related papers
- Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology [2.7280901660033643]
This work explores the scaling properties of weakly supervised classifiers and self-supervised masked autoencoders (MAEs)
Our results show that ViT-based MAEs outperform weakly supervised classifiers on a variety of tasks, achieving as much as a 11.5% relative improvement when recalling known biological relationships curated from public databases.
We develop a new channel-agnostic MAE architecture (CA-MAE) that allows for inputting images of different numbers and orders of channels at inference time.
arXiv Detail & Related papers (2024-04-16T02:42:06Z) - Morphological Profiling for Drug Discovery in the Era of Deep Learning [13.307277432389496]
We provide a comprehensive overview of the recent advances in the field of morphological profiling.
We place a particular emphasis on the application of deep learning in this pipeline.
arXiv Detail & Related papers (2023-12-13T05:08:32Z) - Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for
Deep Learning in Microscopy [44.62475518267084]
This dataset encompasses three image collections in which rodent neuronal cells' nuclei and cytoplasm are stained with diverse markers.
Alongside the images, we provide ground-truth annotations for several learning tasks, including semantic segmentation, object detection, and counting.
arXiv Detail & Related papers (2023-07-26T15:14:10Z) - Deep Learning to See: Towards New Foundations of Computer Vision [88.69805848302266]
This book criticizes the supposed scientific progress in the field of computer vision.
It proposes the investigation of vision within the framework of information-based laws of nature.
arXiv Detail & Related papers (2022-06-30T15:20:36Z) - Learning multi-scale functional representations of proteins from
single-cell microscopy data [77.34726150561087]
We show that simple convolutional networks trained on localization classification can learn protein representations that encapsulate diverse functional information.
We also propose a robust evaluation strategy to assess quality of protein representations across different scales of biological function.
arXiv Detail & Related papers (2022-05-24T00:00:07Z) - Deep Long-Tailed Learning: A Survey [163.16874896812885]
Deep long-tailed learning aims to train well-performing deep models from a large number of images that follow a long-tailed class distribution.
Long-tailed class imbalance is a common problem in practical visual recognition tasks.
This paper provides a comprehensive survey on recent advances in deep long-tailed learning.
arXiv Detail & Related papers (2021-10-09T15:25:22Z) - A review of deep learning methods for MRI reconstruction [8.37609145576126]
A number of ideas inspired by deep learning techniques for computer vision and image processing have been successfully applied to nonlinear image reconstruction in the spirit of compressed sensing for accelerated MRI.
This article provides an overview of the recent developments in neural-network based approaches that have been proposed specifically for improving parallel imaging.
arXiv Detail & Related papers (2021-09-17T15:50:51Z) - Advancing biological super-resolution microscopy through deep learning:
a brief review [5.677138915301383]
Super-resolution microscopy overcomes the diffraction limit of conventional light microscopy in spatial resolution.
Deep learning has achieved breakthrough performance in image processing and computer vision.
We focus on how deep learning advances reconstruction of super-resolution images.
arXiv Detail & Related papers (2021-06-24T14:44:23Z) - A parameter refinement method for Ptychography based on Deep Learning
concepts [55.41644538483948]
coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability.
A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction.
We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
arXiv Detail & Related papers (2021-05-18T10:15:17Z) - Microvascular Dynamics from 4D Microscopy Using Temporal Segmentation [81.30750944868142]
We are able to track changes in cerebral blood volume over time and identify spontaneous arterial dilations that propagate towards the pial surface.
This new imaging capability is a promising step towards characterizing the hemodynamic response function upon which functional magnetic resonance imaging (fMRI) is based.
arXiv Detail & Related papers (2020-01-14T22:55:03Z)
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.