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Learned block iterative shrinkage thresholding algorithm for photothermal super resolution imaging
(2020)
Block-sparse regularization is already well-known in active thermal imaging and is used for multiple measurement based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment. To avoid time-consuming manually selected regularization parameters, we propose a learned block-sparse optimization approach using an iterative algorithm unfolded into a deep neural network. More precisely, we show the benefits of using a learned block iterative shrinkage thresholding algorithm that is able to learn the choice of regularization parameters. In addition, this algorithm enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present the algorithm and compare it with state of the art block iterative shrinkage thresholding using synthetically generated test data and experimental test data from active thermography for defect reconstruction. Our results show that the use of the learned block-sparse optimization approach provides smaller normalized mean square errors for a small fixed number of iterations than without learning. Thus, this new approach allows to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super resolution imaging.
In the present study, a novel and reliable analytical method was developed and validated for the simultaneous determination of 1,3,5-tris(2,3-dibromopropyl)-1,3,5-triazine-2,4,6(1H,3H,5H)-trione (TDBP-TAZTO) and 2,4,6-tris(2,4,6-tribromophenoxy)-1,3,5-triazine (TTBP-TAZ) in environmental samples using high-performance liquid chromatography coupled to a tandem mass spectrometer.
Firstly, for optimization of the liquid chromatography separation, mobile phases, oven temperatures, modifiers, and buffers were varied. Afterwards, the extraction efficiency of sediment and fish samples was tested with different techniques (pressurized liquid, solid-liquid, ultrasound-assisted, and Soxhlet extraction). Additionally, cleanup using modified multilayer silica gel (sediment) and gel permeation chromatography as well as Florisil® columns (fish) with several solvent mixtures were performed. The best results were obtained with the pressurized liquid extraction (optimal conditions: extraction solvent 100% toluene, extraction time 20 min, cycles two, extraction temperature 100 °C, and flushing volume 60%) compared to other solvent extraction methods. On the basis of this optimized analytical procedure, the method was validated with satisfactory values of correlation coefficient (R2) between 0.998 and 0.999 for both matrices in the calibration range of 2.0–502.0 μg kg−1 for TDBP-TAZTO and 16.6–770.6 μg kg−1 for TTBP-TAZ in sediment samples as well as 4.8–303.5 μg kg−1 and 47.4–742.5 μg kg−1 in fish samples (bream), respectively.Mean recoveries (n=5) were calculated for both analytes with spiked matrices at one concentration level (100 μg kg−1) between 98 and 114% with intra-day relative standard deviations less than 11%. The inter-day precision (n = 15) was also acceptable for both compounds < 11%. It was found that the limit of detection and limit of quantification were in the range of 0.4–1.3 μg kg−1 for TDBP-TAZTO and 10–28 μg kg−1 for TTBP-TAZ in surface sediment samples and 7–25 μg kg−1 and 22–80 μg kg−1 in fish samples (bream), respectively. The results indicated that these analytical methods could provide reliable and efficient approaches for quantification of TDBP-TAZTO and TTBPTAZ in sediment and fish samples.
In this work we focus on our most recent studies to super resolution (SR) laser thermography. The goal of SR nondestructive testing methods is to facilitate the separation of closely spaced defects. We explain how to combine laser scanning with SR techniques. It can be shown that stepwise as well as continuous scanning techniques are applicable. Finally, we discuss the effect of experimental parameters and im-age processing techniques to find the optimal SR technique which leads to the highest reconstruction quality within laser thermography.