Adaptive Under-sampling Deep Neural Network for Rapid and Reliable Image Recovery in Confocal Laser Scanning Microscope Measurements
Journal
IEEE Transactions on Instrumentation and Measurement
Journal Volume
71
Date Issued
2021
Author(s)
Abstract
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
Subjects
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
Type
journal article