综述深度学习在显微图像增强中的进展，涵盖超分辨率、重建与去噪。
Recent Advancements in Microscopy Image Enhancement using Deep Learning: A Survey
- 系统梳理深度学习在显微图像超分辨率、重建与去噪中的方法演进
- 总结当前主流技术在提升图像细节与信噪比方面的实用效果
- 适合生物成像与材料科学领域研究者了解前沿技术方向
显微图像增强在理解微观尺度下生物细胞与材料结构细节中起着关键作用。近年来，深度学习方法显著推动了显微图像增强技术的发展。本文综述了该领域的最新进展，聚焦其演进历程、应用现状、面临的挑战及未来方向。核心内容围绕超分辨率、重建与去噪三大关键技术领域展开，分析了深度学习在各领域的当前趋势及其实际应用价值。
原文摘要 · Abstract (English)
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.
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