Recent Advancements in Microscopy Image Enhancement using Deep Learning: A Survey

2025-09-18

AI Summary

Microscopy image enhancement is critical for resolving fine biological cellular and material microstructures. This paper systematically reviews recent advances in deep learning–based approaches for three core tasks: super-resolution, reconstruction, and denoising. Moving beyond existing surveys, we trace the technical evolution of convolutional neural networks (CNNs), generative adversarial networks (GANs), and self-supervised learning methods, analyzing their applicability and limitations in addressing key challenges—particularly multimodal data fusion and real-world deployment. Leveraging standardized benchmarks and empirical results, we comparatively evaluate performance boundaries and typical use cases across methodologies. Finally, we propose a practical technology roadmap tailored to real-world research needs, offering theoretical insights and implementation guidance for algorithm design, cross-modal modeling, and clinical or industrial translation.

Technology Category

Computer Vision: Medical and Biological ImagingMachine Learning: Multimodal LearningIntelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphsSearch and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAGSystems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Surveying deep learning methods for microscopy image enhancement
• Focusing on super-resolution, reconstruction, and denoising techniques
• Addressing challenges and future directions in image enhancement

Innovation

• Deep learning for super-resolution microscopy
• Deep learning for image reconstruction
• Deep learning for denoising microscopy