论文标题
多模式的超级分辨率，用于密集的微观粒度估计
Multi-Modal Super Resolution for Dense Microscopic Particle Size Estimation
论文作者
论文摘要
粒度分析（PSA）是在许多行业中进行的重要过程，可以显着影响最终产品的性质。为此目的的无处不在的仪器是光学显微镜（OM）。但是，OMS通常容易出现缺点，例如低分辨率，小焦点深度和边缘特征由于衍射而被掩盖。我们提出了两个条件生成对抗网络（CGAN）的强大应用，该组合超级解析OM图像看起来像扫描电子显微镜（SEM）图像。我们进一步证明了可以使用自定义对象检测模块，该模块可以在两者面上执行超级分辨颗粒的有效PSA，密集和稀疏的填充图像。从超级分辨图像获得的PSA结果已针对人类注释者进行了基准测试，并从相应的SEM图像获得了结果。提出的模型显示了一种可推广的多模式图像翻译和超分辨率的方法，以进行准确的粒度估计。
Particle Size Analysis (PSA) is an important process carried out in a number of industries, which can significantly influence the properties of the final product. A ubiquitous instrument for this purpose is the Optical Microscope (OM). However, OMs are often prone to drawbacks like low resolution, small focal depth, and edge features being masked due to diffraction. We propose a powerful application of a combination of two Conditional Generative Adversarial Networks (cGANs) that Super Resolve OM images to look like Scanning Electron Microscope (SEM) images. We further demonstrate the use of a custom object detection module that can perform efficient PSA of the super-resolved particles on both, densely and sparsely packed images. The PSA results obtained from the super-resolved images have been benchmarked against human annotators, and results obtained from the corresponding SEM images. The proposed models show a generalizable way of multi-modal image translation and super-resolution for accurate particle size estimation.
