In-situ Detection of Micro Crystals During Cooling Crystallization Based on Deep Image Super-Resolution Reconstruction

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.