Liu, T, Hou, Y, Ma, CY orcid.org/0000-0002-4576-7411 et al. (1 more author) (2017) Sparsity-based image monitoring of crystal size distribution during crystallization. Journal of Crystal Growth, 469. pp. 160-167. ISSN 0022-0248
Abstract
To facilitate monitoring crystal size distribution (CSD) during a crystallization process by using an in-situ imaging system, a sparsity-based image analysis method is proposed for real-time implementation. To cope with image degradation arising from in-situ measurement subject to particle motion, solution turbulence, and uneven illumination background in the crystallizer, sparse representation of a real-time captured crystal image is developed based on using an in-situ image dictionary established in advance, such that the noise components in the captured image can be efficiently removed. Subsequently, the edges of a crystal shape in a captured image are determined in terms of the salience information defined from the denoised crystal images. These edges are used to derive a blur kernel for reconstruction of a denoised image. A non-blind deconvolution algorithm is given for the real-time reconstruction. Consequently, image segmentation can be easily performed for evaluation of CSD. The crystal image dictionary and blur kernels are timely updated in terms of the imaging conditions to improve the restoration efficiency. An experimental study on the cooling crystallization of α-type l-glutamic acid (LGA) is shown to demonstrate the effectiveness and merit of the proposed method.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 Elsevier B.V. This is an author produced version of a paper published in Journal of Crystal Growth. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Crystallization process; Crystal size distribution; Real-time monitoring; Image analysis; Sparse representation; Salience edge |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 Dec 2016 10:38 |
Last Modified: | 20 Sep 2017 04:31 |
Published Version: | https://doi.org/10.1016/j.jcrysgro.2016.09.040 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.jcrysgro.2016.09.040 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:109425 |