https://scholars.lib.ntu.edu.tw/handle/123456789/607427
標題: | Adaptive Under-sampling Deep Neural Network for Rapid and Reliable Image Recovery in Confocal Laser Scanning Microscope Measurements | 作者: | Wu J.-W Chang K.-Y LI-CHEN FU |
關鍵字: | Compressed sensing;Compressive sensing;Confocal laser scanning microscopy;Convolutional neural network;Convolutional neural networks;Current measurement;Deep residual;Estimation;Image quality;Image reconstruction;Temperature measurement;Training;Adaptive optics;Confocal microscopy;Convolution;Deep neural networks;Image registration;Inverse problems;Iterative methods;Recovery;Scanning;Surface analysis;Compressed-Sensing;Confocal-laser-scanning-microscopy;Images reconstruction;Recovery algorithms;Under-sampling | 公開日期: | 2021 | 卷: | 71 | 來源出版物: | IEEE Transactions on Instrumentation and Measurement | 摘要: | Confocal laser scanning microscopy (CLSM) is a non-destructive optical measurement system of high precision, applicable to the construction of three-dimensional topographies of biological cells and engineered materials at the micro- and sub-micro scales. Compressive sensing (CS) has recently been applied in microscope systems to reduce the amount of sampled data required for the reconstruction of images; however, the iterative nature of the CS recovery algorithm imposes high computational complexity. This paper presents an end-to-end non-iterative deep residual convolutional neural network (CNN) applicable to CLSM systems for CS-based reconstruction. In experiments and numerical simulations, the proposed scheme outperformed existing CS recovery algorithms in terms of reconstructed image quality as well as computation time. The proposed algorithm also enabled the reconstruction of images using samples obtained in different regions of an image at various sampling rates to overcome non-uniform information density. The reconstruction performance of the model in terms of robustness and efficiency were validated using real-world CLSM data obtained via random scanning patterns. IEEE |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121354503&doi=10.1109%2fTIM.2021.3134324&partnerID=40&md5=cc2989077a04e112ee583691bfbef719 https://scholars.lib.ntu.edu.tw/handle/123456789/607427 |
ISSN: | 00189456 | DOI: | 10.1109/TIM.2021.3134324 |
顯示於: | 資訊工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。