In this paper, a new image analysis method based on an in-situ microscopic imaging system is proposed for detecting micro crystals in cooling crystallization. Due to the limitation of measurement technology, it is a challenge to extract the evolutionary information of micro crystals, which are too small to be precisely analyzed by in-situ images, e.g. crystals at the initial crystallization stage. An improved deep-learning model is used to enhance the image resolution of micro crystals, thus more effectively obtaining the crystal shape and size information. In addition, a valid size calibration method by simulating particle motion is proposed. Consequently, image size measurement can be easily performed for crystals by using an axis-based algorithm. Experimental verifications on $\beta $ -form L-glutamic acid crystallization were performed to demonstrate the effectiveness of the proposed method for detecting micro crystal information.

中文翻译：

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基于深图像超分辨率重建的冷却结晶过程中微晶原位检测

本文提出了一种新的基于原位显微成像系统的图像分析方法，用于检测冷却结晶中的微晶。由于测量技术的局限性，提取微晶的演化信息是一个挑战，微晶的信息太小而无法通过原位图像精确地分析，例如在初始结晶阶段的晶体。改进的深度学习模型用于增强微晶的图像分辨率，从而更有效地获得晶体的形状和尺寸信息。此外，提出了一种有效的通过模拟粒子运动的尺寸标定方法。因此，通过使用基于轴的算法，可以轻松地对晶体进行图像尺寸测量。的实验验证 $ \ beta $ 形式的L-谷氨酸结晶进行以证明所提出的方法检测微晶信息的有效性。