In-Situ Particle Measurement with Blurred Image Processing Using Telecentric Lenses

Chen Xiaozhen, Zhou Wu*, Liu Hailong, Cai Xiaoshu, Su Mingxu, Cheng Linhu

Institute of Particle & Two Phase Flow Measurement, University of Shanghai for Science and Technology

Shanghai, China *Email: usst_wzhou@163.com

Abstract—A system based on trajectory image processing is developed to achieve in situ measurement of particle size, concentration and velocity in dilute gas-liquid two-phase flow, which is hard to be measured by laser particle analyzer. As the single-frame single-exposure image method, velocities of droplets can be easily calculated from the certain exposure time and the moving distance in a trajectory image, avoiding cross-correlation processing. A telecentric lens is used as an important component of the measurement system which can help to get concentration information. This in situ method avoids inversed processes in measurement methods such as light fluctuation method. Particles with defocused blur are also dealt with in the later part of the paper for further investigation of particle size and concentration measurement.

Keywords-In situ measurement; Telecentric lens; Image processing; Motion blur; Defocused blur

I. INTRODUCTION Particulate two-phase flow has important applications in

engineering, such as oil, atomic energy, aerospace, dynamical and chemical engineering, etc. Study on the complex, nonlinear and dynamic system [1] has significant promotion effects on the development of relative subject.

Although there are many techniques available to measure Particle Size Distribution (PSD), very few offer the opportunity for in-situ measurement without sampling. A variety of new in-situ particle characterization techniques have appeared in recent years, such as the Ultrasonic Attenuation Spectroscopy (UAS) and the Focused-Beam Reflectance Measurement (FBRM) [2]. However, there are certain limitations for instruments based on these methods. UAS and Laser particle size analyzer are not proper for very low particle concentration measurement and the complex inversion algorithm is needed. Neither can they measure particle velocity and the spatial distribution of particle concentration. Particle Image Velocimetry (PIV) is one of the widely used flow measuring instruments. But it cannot measure the concentration and size of the droplet. The high price also makes it unachievable in in-situ measurement. Phase Doppler Particle Analyzer (PDPA) can be used in simultaneous measurement of particle size, velocity and concentration [3-6], but it is one point measurement method and cannot be used in situ.

In this paper, we use image analysis to automatically extract the maximum possible information from in-situ digital particle images, and use it to monitor particle shape and size distribution. This design of image-based method of in-situ measurement system has advantages of simple structure and easy installation. It can be applied in a complex industrial environment and keep stable operation to achieve in-situ measurement.

In this paper, a brief introduction of principles is given for measurement of particle size, velocity and concentration. Then the specific image processing is explained in detail. Next, we present the experimental system and validation of the method. Then studies dealing with defocused blur images are discussed for further investigation.

II. PRINCIPLES OF MEASUREMENT

A. Measurement of particle size and velocity The essence of CCD imaging is the integral of light energy

on the photosensitive element within the exposure time M, with the signal being quantized into gray value of image. If the measured object is stationary, the light energy distribution has nothing to do with the time. If the measured object is moving, image of the object will have a trail in the moving direction, as shown in Fig.1. As long as the particles are in the depth of field range of the camera, blur of particle images are generally motion blur instead of defocus blur.

Fig. 1 Motion-blurred image and its binary image after processing

Under the action of surface tension, liquid droplets are generally spherical and the velocity is 0 in the direction which is perpendicular to the movement. Therefore the width of captured particles smear image can be approximated as the droplet particle diameter. It also includes the relative displacement of particles and camera in the blurred image. Particles within the exposure time is assumed for the uniform motion, then you can get the velocity of the particle as long as

Project supported by the State Key Program of National Science Foundation of China (50836003) and Science and Technology Support Program by Shanghai Science and Technology Committee (10540501000)

978-1-4577-1775-8/12/$26.00 ©2012 IEEE

the distance of particle movement in the exposure time is known [7]. By image processing algorithms, identified particles are equivalent to the oval and its long axis and short axis can be obtained. The short axis can be approximated as the particle diameter D, the long axis is the length S of blurred image, as shown in Fig. 2. The distance of particle movement is L within exposure time and it has L=S-D from Fig. 2. If exposure time is known, particle velocity can be obtained by:

L S DvM M

−= = Eq

(1)

Fig. 2 Physical model of motion-blurred images

Calibration of the optical measurement system is done with a micrometer by obtaining the ratio of one pixel size to the actual object size corresponding to one pixel. That is known as magnification. Accuracy of measured particle size is related to that ratio. Smaller pixel size and larger magnification can get a higher accuracy and resolution of granularity. Appropriate exposure time is also needed. If the exposure time is too short, length of particle movement will be too small to be detected. Furthermore, a lot of noise will appear on the images. If the exposure time is too long, the particle smear may be out of the view of the field. And we should adjust the exposure time according to the picture displayed on the computer screen to make appropriate contrast between background and particles.

B. Measurement of concentration We can obtain the information of particle numbers, particle

size and velocity distribution in the field of view by fuzzy image processing. If the space volume of particles in the field of view is known, concentration of the particles in the space can be obtained. Assuming the width W, the height H and the lens depth of field lδ , volume of the spatial region V can be acquired as, * * lV W H δ= Eq (2)

So, we can get the information of particle concentration as long as the clear images of particles are taken into account. Assuming N for the total number of particles, )(nD for size of the n-th particle and Z for the total number of frames of the image for statistics, the particle volume concentration C can be calculated as,

3

1

( ) / ( * )6

N

n

D nC Z Vπ=

=∑ Eq (3)

III. SPECIFIC IMAGE PROCESSING In order to make sure the measurement accuracy of

particles parameters, image pre-processing such as the segmentation of adhesive particles is necessary. In addition, in order to improve processing speed and achieve requirement of real-time in-situ measurement, we put continuous nine images together into a 3×3 matrix so that it can process much faster. The main process is shown in Fig.3.

Fig. 3 The processing of the detection of particle’s parameters

In order to realize effective segmentation and recognition of the images, we first need to improve the uneven illumination and exclude the noise of the images. To remove image noise and enhance image, the typical median filter [8] is applied in this paper. Then three different methods are adopted for binarization processing to obtain the best result. They are Otsu method [9], maximum entropy method [10] and artificial given threshold of binarization for choices in the image processing of the measurement system.

Because of image noise, there may be occasionally a handful of the dark similar to small particles after filtering. The light source and non-uniform two-phase flow may lead to the uneven image background so that causes a large area of shadow in the image after doing the binarization processing, these conditions should be done before the survey related to treatment. We should deal with these conditions before statistics. The threshold that is used to remove the large areas of shadow can be calculated using the following method: calibrating for the field to get the actual particle size of a pixel according to camera’s multiple, and just take an pixel value that is bigger than probably size range of the measured particles appropriately. In addition, because the view size is limited, part of the particles may be ghosting longer due to the movement velocity so that one part is in view and the other

part is out of the field. Such particles should be removed in order to obtain accurate measurement of particle size distribution. The removal of incomplete particle may bring a certain extent to the statistical results. By this method, in general, the result of concentration is small compared with the real concentration of the particle. So only a large number of experiments are required to the empirical compensation at present and it needs more discussion. All these processes can be automatically performed and the typical processing time for obtaining particle size, concentration and velocity is 5 seconds by the computer which has 2.00 GB of RAM and Inter (R) Core(TM) 2 Duo @ 2.00GHz CUP.

The velocity of moving particles can be measured by image analysis function ‘regionprops’ in the Matlab Image Processing Toolbox [11]. Motion information of particles is acquired through the access to the long axis a, minor axis length b and ‘smear’ angle with the x-axis. The velocity is

bba /)( − and the direction isθ .

IV. EXPERIMENTS Experimental verification of this system is operated on a

gas-liquid cyclone. Cyclone separator is a kind equipment to make the discrete phase particles separate from mainstream under the action of centrifugal force by using the rotary motion of the two phase flow. As a separate device, cyclone separator has the advantages of no moving parts, simple structure, stable operation, small power consumption and high separation efficiency compared with other separation devices, such as filtration and settlement. Cyclone has a wide range of applications in petroleum, chemical industry, building materials and the environmental protection industries.

The most common cyclone separator is liquid-vapor cyclone separator. Droplet has some unique properties, which are different from solid particles, such characteristics are as follows:

1) When contacting a solid surface, solid particles usually bounce out while droplets usually attach on the surface to form a liquid membrane and spread around along the solid surface. In most cases, the moving direction of the droplets and the airflow movement are in opposite directions.

2) Droplets will merger and form a larger one after collision between droplets.

3) Droplets and liquid can be easily breached under shear forces and be split into smaller droplets.

These features will affect the separation efficiency of gas-liquid cyclone. Separation efficiency evaluation is an important index of the cyclone separator. It is related to the droplet size, velocity and concentration on the inlet and outlet of the cyclone.

A. Cyclone system Schematic diagram of the cyclone testing system is shown

in Fig. 4. Liquid particle parameters on the inlet of cyclone are measured. Water supply to the nozzle is adjusted by the dose pump. Air compressor provides compressed air as the driving force of the nozzle. Spray droplets are transported by the pipeline from the buffer tank into the cyclone. Different

cyclone inlet parameters are acquired under different conditions by adjusting the dose pump. Photo of the cyclone system is shown in Fig. 5.

Fig. 4 Schematic diagram of the cyclone system

Fig. 5 Photo of the cyclone system

B. Measurement system The in-situ measurement system developed using image-

based method consists of image acquisition system, the camera moving control systems and computer image processing system, as shown in Fig. 6.

Fig. 6 In-situ measurement system architecture based on image method

Image acquisition system consists of industrial digital CCD camera, telecentric lens, high-power LED. In this paper, the frame exposure mode is chosen for the CCD camera which can capture images of fast-moving particles. Capturing a clear picture of fast moving particles requires very short exposure time of the camera or strobe light. Brighter light source is needed when a short exposure time is used. In general, high-power lasers are taken into account. However, this increases the complexity of the system and the cost increases significantly. Moreover, using strobe light needs synchronization circuit design. To reduce the cost and complexity of the system we applied the fuzzy image processing methods. The minimum exposure time of CCD camera is 22μs in this paper, and the maximum resolution is 1392×1040. Information of the particle size, concentration, and velocity is obtained by blurred particle image processing. Not only the system becomes simple and cheap, but also the velocity can be measured without cross-correlation.

Camera interface in this article is Gigabit Ethernet interface which has a fast transfer rate (1000Mbps). The coaxial cable can support a maximum distance of 100m so that the remote control of camera image can be achieved in the industrial field and efficient transmission.

As the depth of field presence, ordinary lens imaging will lead to a certain degree of distortion of objects, which produce the phenomenon that is "everything looks small in the distance". Position of the measured particles in the measurement body is random so the size of particles in the CCD camera imaging is decided by two factors, size of the particle itself and the distance between camera and particles. Thus the particle size cannot be accurately determined by CCD camera images produced using ordinary lenses. A usual way to solve this problem is to limit depth of field of the lens which can reduces the depth of the measured body to ensure that the measured particle’s positions are essentially the same in the measured body. It can make sure that the image size is only related with the measured particle size. But this will make appear that the number of particles measured in a sharp decrease in the measured body. It is bound to a substantial increase in the captured frames of image and extends the measurement time in order to ensure a certain number of particles images, which resulted in lower efficiency of real-time measurement and increasing the workload of image processing. However, if the aperture of the lens is set in object focal plane of the optical path that can reduce such errors, making the particles not occur larger and smaller in the images because of their different positions in depth of field. The lens designed according to this principle is called telecentric lens. In this paper, in order to ensure the images of the particle does not occur the phenomenon of being larger and smaller than the particles’ actual sizes in anywhere of the measured body, we use the telecentric lenses which can also improve the measurement accuracy. Fig. 7 is the light path comparison of non-telecentric optical and telecentric optical [12]. The choice of the magnification of telecentric lens depends on the estimated value of the measured particle size. Generally, the lens’ magnification is first calibrated by the micrometer ruler and then used for the experiments.

Fig. 7 The schematic of telecentric lens: (a) Non-telecentric optical, and (b)

telecentric optical [11].

The system uses a light source of high-power LED that the power is 3W. Light source is located on the front of the lens through the bracket while frame of a fixed light source is fixed with CCD camera. Thus, the images still can be maintained without shaking to ensure the stability of the system even in the case of shock. The relative distance of the light source and CCD is fixed in the process of moving the CCD. The background is always uniform when it is measuring to ensure the quality of the images that are collected in the field measurements and the accuracy of particle identification in image processing.

To study the mechanism of separation of particles in the measurement body, we need to understand the parameters of particles. To meet the requirements, we designed a camera displacement control system which can control the camera moves along the axial so that can measure the parameters of the particle along the radial position of the separation. The system is mainly composed of rail, stepper motor and equipment control box. Stepper motor controls the movement of the camera on the rail while the CCD is fixed on the rail along with the rail’s movement to measure different regions of space. Stepper motor has the control accuracy of 1.6 microns which is driven by the equipment control box. The device control box is connected to the computer via USB while the communication between the CCD and computer via Gigabit Ethernet, and ultimately realize image acquisition through the control of the CCD camera’s displacement and the computer.

C. Experimentsal results Fig. 8 shows the images of water droplets in a certain

condition, the exposure time is 22μs when shot these images. We can get the information about size, concentration and velocity of the particles through the process and analysis of the obtained images.

Fig. 8 The images of moving droplets

Fig. 9 is particle size distributions when the metering pump comes in different stalls. Table 1 shows the standard deviation of particle size in the different four conditions. Measurements indicate that the particle size distributions are wide in different conditions but are mainly concentrated in the 5-10μm range, the number of larger droplets is small. After our analysis, the effect of the nozzle’s spray is poor and the droplets collide with each other to form large droplets may be the main reasons.

Condition 1 Condition 2

Condition 3 Condition 4

Fig. 9 Distributions of particle size under different conditions

Table 1 Standard deviation of particle size in the four conditions

condition condition 1

condition 2

condition 3

condition 4

average particle size(μm) 9.2 9.6 5.8 9.7

standard deviation(μm) 5.6 5.8 2.8 5.2

Table 2 is the measured concentration values of each condition. The experimental system can adjust the water inflow by the dose pump, and then changed the concentrations. As long as the nozzle exit pipe is long, with the role of the buffer tank, the concentration did not change significantly. The measured concentration values of each condition are roughly the same as the flow of different stalls of the metering pump.

Table 2 Concentration values of each condition

condition condition 1

condition 2

condition 3

condition 4

Concentration (p.p.m.) 13.31 15.33 20.63 18.98

As for the piston air compressor whose air flow is instability provide gas for the nozzle, the change of the particle velocity is large versus time, Fig. 10 is the measured particle’s velocity distribution in a random moment, the velocity is mainly between 0.5-1.5m/s. The above results are consistent with the actual situation of the equipment.

Fig. 10 Distribution of particle velocity for a certain instant

Fig. 11 Clear image of measured droplets

Fig. 11 shows the clear image of the measured droplets which we can find the measured droplets are like radiate spherical, the ratio of the minor axis and major axis is about equal to 1. This basically can prove the algorithm that the fuzzy image of the particles is equivalent to an ellipse is reasonable and feasible.

V. DEFOCUS BLUR IMAGES Fig.11 also shows that a few defocus burred particle

images are captured together with clear images, since a LED light is used instead of laser. These defocused particle images are not particularly removed during the image processing described above. A part of the images are ignored during the binarization processing and the others are dealt with as clear images, with the particle size strongly affected by defocus degree and binarization threshold. Furthermore, these are images of particles out of the depth of field, calculated concentrations values will be enlarged including these defocused images. Analyzing of the defocus images are very important for image processing. Correct particle size analyze from defocus images will be significant for particle size distribution.

In this paper, simulated defocused images are used for investigation of particle size measurement. Fig. 12(a) shows simulated defocused image of a circle particle with radius 75 Pixels and disc burred parameter 5 pixels. Fig. 12(b) shows the gray value and gray gradient distribution along the diameter of the blurred circle image, calculated by Kirsh operator. The results indicate that the distance between two point of maximum gradients can represent the diameter of the particle.

(a) Defocus image of circle

0 50 100 150 200 250

0

50

100

150

200

250

Gra

dian

t

Gra

y

Position/Pixel

Gray Gradiant

0

300

600

900

1200

1500

(b)Gay and gradient distribution along diameter

Fig.12 Simulation analysis

To further investigate this particle restoration method, defocused particle images are acquired from real experimental work, schematic diagram of which is shown in Fig.13. Particle samples with diameter 0.7 mm are laid on the stage and it can

move forward or backward to reach different defocused degree. Nine defocused particle image with different defocus degree are acquired, three of which are shown in Fig.14.

Fig.13 Schematic diagram of experiments

focus plane defocus 2mm defocus 4mm

defocus 6mm defocus 8mm defocus 9mm

Fig.14 Defocused images of particle on different defocus distance

Table 3 Maximum gradient distribution Defocus

distance/mm 0 1 2 3 4 5 6 7 8 9

Coordinate /Pixel

x1 50 45 54 59 54 57 51 62 65 54 x2 199 199 197 202 207 214 209 206 207 229

Diameter/Pixel 149 154 143 143 153 157 153 144 142 175

Difference/% -0.67 2.67 -4.67 -4.67 2.00 4.67 5.33 -4.00 -5.33 16.67

Table 3. illustrates the coordinate values x1 and x2 according to the two maximum gradients along the particle diameter direction. It shows for this experimental system that when the defocus distance in smaller than 8 mm, the maximum error of particle diameter results obtained through this method is 5.33%. As the distance gets larger, transition zone of the image gets larger and gray gradient brought in by noise gets near to that in transition zone. That leads to larger error when detecting particle edge using gradient value. It means that results are dependable for this system when the defocus distance is smaller than 8 mm.

For further studies, this method is expected to be applied to analyze the images acquired in the former part of the paper, for example Fig.11, to get more accurate particle size distribution.

VI. CONCLUSIONS In this paper, considering the characteristics of dilute

particulate two-phase flow, we developed an in-situ particle measurement system using telecentric lens with blur image processing. It can simultaneously measure size, concentration and velocity of particles with simple device. The experimental results for inlet of a gas-liquid cyclone indicate the possibility

of this image method. The key points of the experiments are making uniform background light source and detecting the edge of defocused particle image. By using the method proposed in this paper, morphology analysis of the measured particles can also be done in further researches. This work can draw the following conclusions: 1) The image method can effectively measure the particle

size, velocity, concentration of dilute particulate two-phase flow. For the conditions investigated in this paper, 5-10 μm particle size, 0.5-1.5 m/s velocity and 13-21 ppm concentration are obtained.

2) The key of image processing is the extraction of the objects. Different pretreatment and different threshold calculation methods should be used for different lighting conditions. Adhesion phenomena are not obvious in this research but will be important in conditions with larger concentration.

3) Defocused particle images can be processed to obtain more accurate size information by image restoration based on gray gradient of image. The method is expected to be applied to analyze motion and defocus blurred images in further study.

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