Since target information can be extracted from the light scattering distributions, light scattering from randomly rough surface has been studied in details. As numerical light scattering computation methods can provide precise information avoiding complicated operations and expensive experimental setups, various numerical methods have been widely used. However, most of them require ensemble average computation to obtain the stable results, inevitably decreasing the calculation efficiency. In order to further accelerate the processing speed by avoiding the ensemble average calculation, a high speed method based on surface slope probability density function is designed in this paper, which using the statistical parameters of randomly rough surfaces for direct light scattering calculation. With numerical simulations proving its high accuracy and rapid speed in light scattering computation, the slope probability density function based method is a potential tool for light scattering computation and analysis.
As an important marker in disease diagnosis, red blood cell morphology measurement is necessary in biological and medical fields. However, traditional setups as microscopes and cytometers cannot provide enough quantitative information in morphology detections. In order to capture tiny variations of red blood cells affected by metal ions in external environment, quantitative interferometric microscopy is applied: combining with phase retrieval and cell recognition, cellular phases as well as additional quantitative cellular parameters can be acquired automatically and accurately. The research proves that quantitative interferometric microscopy can be potentially applied in cellular observations and measurements for both biological and medical applications.
In order to quantitatively analyze scattering from two dimensional randomly rough Gaussian surfaces, Kirchhoff approximation method is adopted in numerical calculation for analyzing full angular Stokes vectors of light scattering. With studying both the p- and s-polarized scattering fields from various materials such as metals and dielectrics, it is found that V components of scattering light from metals and dielectrics are different. Via analytical calculation according to slope probability density, the V component difference is attributed to refractive index of materials. Both numerical and analytical calculations prove the V component difference in light scattering can act as a criterion for metal and dielectric identification.
In order to obtain quantitative phase distributions from interferograms, phase retrieval composed of phase extracting and unwrapping is adopted in quantitative interferometric microscopy. However, phase unwrapping often requires a long time, limiting applications such as high-speed phase observations and measurements. In order to accelerate the processing speed, a phase unwrapping free Hilbert transform (HT)-based phase retrieval method is proposed. Though another background interferogram without a sample is needed, phase unwrapping can be omitted, saving a large amount of time for phase recovery. Additionally, the proposed HT-based method can maintain more sample details, thus providing high-accurate quantitative phase imaging. Considering its fast speed and high accuracy in phase retrieval, it is believed that the unwrapping free HT-based phase retrieval method can be potentially applied in high throughput cellular observations and measurements.
Erythrocyte morphology is an important factor in disease diagnosis, however, traditional setups as microscopes and cytometers cannot provide enough quantitative information of cellular morphology for in-depth statistics and analysis. In order to capture variations of erythrocytes affected by metal ions, quantitative interferometric microscopy (QIM) is applied to monitor their morphology changes. Combined with phase retrieval and cell recognition, erythrocyte phase images, as well as phase area and volume, can be accurately and automatically obtained. The research proves that QIM is an effective tool in cellular observation and measurement.
Quantitative interferometric microscopy is used in biological and medical fields and a wealth of applications are proposed in order to detect different kinds of biological samples. Here, we develop a phase detecting cytometer based on quantitative interferometric microscopy with expanded principal component analysis phase retrieval method to obtain phase distributions of red blood cells with a spatial resolution ~1.5 μm. Since expanded principal component analysis method is a time-domain phase retrieval algorithm, it could avoid disadvantages of traditional frequency-domain algorithms. Additionally, the phase retrieval method realizes high-speed phase imaging from multiple microscopic interferograms captured by CCD camera when the biological cells are scanned in the field of view. We believe this method can be a powerful tool to quantitatively measure the phase distributions of different biological samples in biological and medical fields.
The paper proposed a simple large scale bio-sample phase detecting equipment called gravity driven phase detecting cytometer, which is based on quantitative interferometric microscopy to realize flowing red blood cells phase distribution detection. The method has advantages on high throughput phase detecting and statistical analysis with high detecting speed and in real-time. The statistical characteristics of red blood cells are useful for biological analysis and disease detection. We believe this method is shedding more light on quantitatively measurement of the phase distribution of bio-samples.
Quantitative interferometric microscopy is an important method for observing biological samples such as cells and tissues. In order to obtain continuous phase distribution of the sample from the interferogram, phase extracting and phase unwrapping are both needed in quantitative interferometric microscopy. Phase extracting includes fast Fourier transform method and Hilbert transform method, etc., almost all of them are rapid methods. However, traditional unwrapping methods such as least squares algorithm, minimum network flow method, etc. are time-consuming to locate the phase discontinuities which lead to low processing efficiency. Other proposed high-speed phase unwrapping methods always need at least two interferograms to recover final phase distributions which cannot realize real time processing. Therefore, high-speed phase unwrapping algorithm for single interferogram is required to improve the calculation efficiency. Here, we propose a fast phase unwrapping algorithm to realize high-speed quantitative interferometric microscopy, by shifting mod 2π wrapped phase map for one pixel, then multiplying the original phase map and the shifted one, then the phase discontinuities location can be easily determined. Both numerical simulation and experiments confirm that the algorithm features fast, precise and reliable.
The statistical distribution of natural phenomena is of great significance in studying the laws of nature. Here, in this paper, based on laser scattering particle counter, a simple random pulse signal generating and testing system is designed for studying the counting distributions of three typical objects including particles suspended in the air, standard particles, and background noises. Moreover, in order to have a deep understanding of the experimental results from laser scattering particle counter, a random process model is also proposed theoretically to study the random law of measured results. Both normal and lognormal distribution fittings are applied to analyze the experimental results, and we have proved that statistical amplitude and width distributions of particles suspended in the air, standard particles, and background noise match well with lognormal distribution when natural numbers are used as the variables. This study is an important reference for statistical data processing for laser scattering particle counter, moreover, it will also be a useful guide for designing laser scattering particle counter with high accuracy and processing speed.
Volume Moiré Tomography (VMT) is an important technique to diagnose the flow field. In this Letter, the characteristic of temporal phase-shifting is analyzed for VMT. When the distance between two cross gratings is not on the Talbot distance, the phase-shifting factors are existed between moiré patterns of different orders. Especially, when the distance conforms to the sub-Talbot distance, the phase-shifting factors are maximum. This characteristic of temporal phaseshifting could be used for real 3-D flow fields reconstruction in the future.
Single-shot quantitative interferometric microscopy (QIM) needs a high-accuracy and rapid phase retrieval algorithm. Retrieved phase distributions are often influenced by phase aberration background caused by both imaging system and phase retrieval algorithms. Here, we propose an improved phase aberration compensation (PAC) approach in order to eliminate the phase aberrations inherent in the data. With this method, sample-free parts are identified and used to calculate the background phase, reducing phase errors induced in samples and providing high-quality phase images. We now demonstrate that QIM based on this PAC approach realizes high-quality phase imaging from a single interferogram. This is of great potential for a real-time speedy diagnosis.
Mueller matrix is a useful tool for analyzing polarization characteristics in a wealth of research fields. With Mueller
matrix, the modulation effects of samples on polarization could be quantitatively analyzed and discussed. In this paper,
all elements in the Mueller matrix are calculated when the lights scatter from one dimensional randomly rough surfaces
at different conditions with Kirchhoff approximation method which owns high accuracy and fast calculation speed.
Besides, theoretical analysis of the light scattering from randomly rough dielectrics and metal surfaces is also proposed
in this paper. Moreover, with both theoretical analysis and numerical simulations, we have explained the variations of all
elements in Mueller matrix, more importantly, m34 is highly focused which is quite a significant mark in both randomly
rough dielectric and metal surfaces. To our best knowledge, it is the first time this obvious difference is both analyzed
and discussed via both theoretical analysis and numerical calculation, and is successfully explained via phase difference
between incident and reflective waves. According to the analysis, more information of the target could be obtained in
order to determine the characteristics of the target. The paper will be an important reference for polarization imaging in
laser radar and remote sensing, etc.
Real images usually have two layers, namely, cartoons(the piece-wise smooth part of image) and textures(the oscillating
pattern part of the image). In this paper, we solve the challenging image deconvolution problems by using variation
image decomposition method which can regularize the cartoon with total variation and texture in G space respectively.
Different from existing schemes in the literature which can only recover the smooth structure of the image, our
deconvolution method can not only restore the smooth part of image but also recover the detailed oscillating part of the
image. Numerical simulation examples are given to demonstrate the applicability and usefulness of our proposed
algorithms in image deconvolution.
Underwater laser imaging is of great significance in underwater search and marine science, etc. However, traditional
underwater laser imaging is often of poor quality with noises and blurs, moreover, the resolution of the image is also
low. In order to obtain clear underwater images with high resolution and quality, here, we have designed a range gated
imaging underwater imaging system and realized an image restoration approach. In this paper, based on the introduction
to the imaging system and image restoration algorithm, the experiment is established by setting the imaging system
under water in the lake to capture the underwater targets. With the proposed underwater image restoration approach,
images of high quality could be retrieved which proves that the method is able to identify the target ~10 meters away
underwater.
In order to study light scattering from randomly rough surface, the linear filtering method is used to generate Gaussian randomly rough surface, and the method of moments is used to calculate the scattering light intensity distribution from perfect conduct and dielectric surfaces. The calculation results show that scattering characteristics between conductor and dielectric surfaces exist several significant differences: (1) the scattering peak value of perfectly conduct is larger than scattering peak value of dielectric on the same roughness; (2) the difference between s- and ppolarized scattering results are rather small in perfectly conduct randomly rough surfaces, while there is a obvious difference between s- and p-polarized scattering results in the condition of dielectric randomly rough surfaces; (3) though in both conditions of perfectly conduct and dielectric randomly rough surfaces, there is a shift from specular to backscattering direction when incident is p-polarized light, however, in dielectric randomly rough surface situation, the shift is much more obvious than in conduct situation.
In order to study and improve atmospheric and air pollution monitoring sensor, a new
mathematical model of random signals is established based on measuring process of light
scattering signals analyzed by laser particle counter which combines the high speed data
acquisition card PCI-9812 and optical particles counting sensor. The measured random signals
can be divided into stability constant part and random variation part. The performance of the
instrument is improved by both this model and analytical methods. Statistical distributions of the
amplitude of the standard particles with different diameters are studied by the original experiment
and improved one. The resolving power of particle size could attain more than 90%. The results
reveal statistical distributions match well with lognormal distribution with a natural number as an
independent variable. The lognormal distribution plays an important role in describing the
random fluctuation characteristics of random process in both theories and experiments.
Furthermore, both normal and lognormal distribution fitting are applied in analyzing the
experimental results and testified by chi-square distribution fit test and correlation coefficient for
comparison.
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