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Application of Blind Deconvolution Based on the New Weighted L1-norm Regularization with Alternating Direction Method of Multipliers in Light Microscopy Images

Published online by Cambridge University Press:  11 September 2020

Ji-Youn Kim
Department of Dental Hygiene, College of Health Science, Gachon University, 191, Hambakmoero, Yeonsu-gu, Incheon, Republic of Korea
Kyuseok Kim
Department of Radiation Convergence Engineering, Yonsei University, 1, Yonseidae-gil, Wonju-si, Gangwon-do, Republic of Korea
Youngjin Lee*
Department of Radiological Science, College of Health Science, Gachon University, 191, Hambakmoero, Yeonsu-gu, Incheon, Republic of Korea
*Author for correspondence: Youngjin Lee, E-mail:
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This study aimed to develop and evaluate a blind-deconvolution framework using the alternating direction method of multipliers (ADMMs) incorporated with weighted L1-norm regularization for light microscopy (LM) images. A presimulation study was performed using the Siemens star phantom prior to conducting the actual experiments. Subsequently, the proposed algorithm and a total generalized variation-based (TGV-based) method were applied to cross-sectional images of a mouse molar captured at 40× and 400× on-microscope magnifications and the results compared, and the resulting images were compared. Both simulation and experimental results confirmed that the proposed deblurring algorithm effectively restored the LM images, as evidenced by the quantitative evaluation metrics. In conclusion, this study demonstrated that the proposed deblurring algorithm can efficiently improve the quality of LM images.

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Copyright © Microscopy Society of America 2020

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