基于深度学习的显微镜图像增强最新进展：综述
Recent Advancements in Microscopy Image Enhancement using Deep Learning: A Survey
AI总结：
本文综述了深度学习在显微镜图像增强中的最新进展，涵盖超分辨率、重建和去噪三大领域，总结了其演变、应用、挑战及未来方向。
摘要：
显微镜图像增强在理解微观尺度下生物细胞和材料的细节方面发挥着关键作用。近年来，显微镜图像增强技术取得了显著进步，特别是在深度学习的帮助下。本综述旨在提供这一快速发展的前沿方法的概览，重点关注其演变、应用、挑战和未来方向。核心讨论围绕显微镜图像增强的关键领域展开，包括超分辨率、重建和去噪，每个领域都探讨了其当前趋势以及深度学习的实际效用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
