Fiber Optic Specklegram Sensor Based On Image

10th Iranian Conference on Machine Vision and Image Processing, Nov. 22-23, 2017; Isfahan Univ. of Technology, Isfahan, Iran

Fiber Optic Specklegram Sensor Based On Image Processing Using LoG Filter Mostafa Al Zain Department of Electrical Engineering Faculty of Engineering University of Isfahan Isfahan, Iran [email protected]

Payman Moallem Department of Electrical Engineering Faculty of Engineering University of Isfahan Isfahan, Iran [email protected]

Hamidreza Karimi-Alavijeh Department of Electrical Engineering Faculty of Engineering University of Isfahan Isfahan, Iran [email protected]

Alireza Khorsandi Department of Physics Faculty of sciences University of Isfahan Isfahan, Iran [email protected]

recording specklegram evolution, which is obtained by generating different heating, and cooling sweeps [11]. A structure of Singlemode-Multimode-Singlemode fibers can be used to sense high frequency or micro-vibration (below one micron) [12]. Sandwiching a polymer optical fiber (POF) between two SMFs construct single-mode-multimode-singlemode structure in order to provide a large dynamic range when measuring strain [13]. Sharp mode filtering identifies heating and cooling phase in a way that can produce an interference pattern of only one lobe along an elliptical trajectory. This allows measuring the temperature variation of less than 1 K per meter [14]. These sensors can be also used in other applications such that measuring the properties of liquid. By processing the inner product of speckle patterns with artificial neural networks, the mass and viscosity of water and ethanol can be measured. The absolute error in these sensors is lower than 0.5 g and 0.1 mPa s of the mass and viscosity respectively [15]. When the speckle patterns of orthogonal polarizations are processed separately, the spectrum reconstruction error is decreased and the bandwidth is increased [16]. Moreover, the hand postures that are based on the force myography measurements are identified using fiber specklegram sensor with a 91.3% accuracy. The intensity of the output speckle changes with the change of the microbending transducers that are attached to the user forearm [17]. In fiber bending loss sensors, the speckle image is divided into blocks to increase the dynamic range. The dynamic range is increased by 237.5% for a 5*5 grid. The supposed filtering depends on the 2D Discrete Wavelet Transform (DWT) transformation theory [10]. The use of Laplacian of Gaussian (LoG) filter improves the intensity difference between normal and abnormal state images leading into an increase in the dynamic range and accuracy of the sensor.

Abstract In fiber optic sensors, laser source produces a coherent light which is transmitted through a fiber cable. The output of this laser forms a speckle. This speckle is captured by a CCD and then analyzed by a PC. The intensity and shape of the speckle change when external force is applied on the cable. In this paper, Laplacian of Gaussian (LoG) filter is used to improve the dynamic range and accuracy of the fiber optic sensor. Capturing images is done in a dark room and the simulation is done by Matlab. The difference between the intensities of a normal and abnormal state image was 4.0556 when using 2D Discrete Wavelet transform (DWT) filter. It is increased into 14.6191 when LoG filter is used, thus increasing the dynamic range and accuracy of the sensor. In addition, the time is decreased by about 24.5 %. The number of changed blocks is increased by about 22 % leading to double increase in the accuracy and dynamic range. Keywords speckle imaging; fiber optic sensors; image proccesing; fiber specklegram; LoG filter

I. INTRODUCTION In optical fiber systems, the number of modes that are transmitted through a fiber cable is detected by the diameter of this cable (d) and the critical angel of the incident light, which is represented by another factor, called NA. Depending on the value of these parameters (d and NA) fiber can be classified into Single Mode (SMF) or Multimode fiber (MMF). When a MMF transmits light from a laser source, an interference between these modes is occurred. This interference forms a speckle that is shown at the opposite side of the laser source [1,2]. In fiber optic sensors, perturbations change the index of refraction of fiber cable. Therefore, the interference between the modes that are transmitted by MMF is changed [3]. This interference change leads to another change in the output speckle that is captured by CCD. Using image processing techniques, the change in the shape and intensity of the speckle is detected and analyzed to obtain information about perturbation [4,5]. Artificial intelligence is used to get more information from this speckle image. For example, global image differencing and global correlation can be used in this sense [5]. Perturbation can be one or more of the following parameters: temperature [6-8], vibration [9] or bending loss [10]. High temperatures ( ) can be sensed by

II. ANALYSIS The output of a coherent light source that is transmitted through a MMF forms an intermodal interference patterns or a speckle in the opposite side of the cable. The shape of the speckle changes with the change in the index of refraction, which is, in its turn, a result of a perturbation. This change in the index of refraction is considered as a limiting factor in the fiber

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communication system. However, it can be used in fiber sensors to detect perturbations such as vibration, temperature, strain and bending loss changes. The far field complex speckle distribution of the fiber according to [1] is given by (1) where a and are the amplitude and phase of the mth mode, respectively and M is the number of modes. The intensity of the output of the speckle is expressed by

(2) The application of external stimuli will affect the field complex speckle distribution. Therefore, it is rewritten as Fig. 2. Change of the NIP factor with respect to bending loss radius.

(3)

The dynamic range limitation represents a big challenge in these kind of sensors. After a certain value of perturbation, it is impossible to detect any more perturbations. That is because the NIP of the image may be decreased in one pixel but increased in another one. In [2], Eric Fujiwara et al. suggested an idea to overcome this limitation. In this sense, they proposed that dividing image into sub-images or blocks will create local NIP for every block. By this way, the dynamic range is increased. A definite NIP in one block will only decrease with the increase of the external stimuli. This decrease is not affected by any possible increase of the NIP in other blocks. In [2], the author used a 2D DWT filter.

a represent the amplitude and phase deviation, respectively. The intensity of the stimulated output is reformulated to (4) According to [1], NIP is defined as .

(5)

The NIP of an image which is captured by a CCD, represents a good parameter to sense external stimuli because it is changed with perturbation such as strain, temperature or bending loss. The block diagram in Fig. 1 represents a common system modal to capture the NIP and sense perturbation.

However, the dynamic range can be improved using LoG filter, which means that more perturbation can be detected. Moreover, due to the increase in the difference between I0 and I, the spatial resolution of the perturbation, sensitivity and accuracy are increased. This improvement is due to the fact that LoG filter smooths images, because it consists of Laplacian filter, next the derivation of this smoothed image is taken. In this application, changes between normal and abnormal state images can be better shown when using LoG filter to that when the 2D DWT filter is used. III. USING LOG FILTER TO IMPROVE THE DYNAMIC RANGE After capturing the speckle images by a CCD, the speckle change is processed and analyzed by PC using Matlab. The image is divided into sub-images or blocks. The most significant block is defined by the one that has the biggest difference between and . Using this block the maximum available dynamic range is obtained. If the difference between and is bigger than for a certain block, where is constant defined by the user, then it can be said that this is an accepted block. If the number of accepted blocks is greater than a definite ratio of the overall blocks then the perturbation that is detected could be considered as a real perturbation. If not, the perturbation that occurs could be considered as a noise. For example if the number of the blocks that have are half or one third of the overall blocks then we consider that a perturbation is done. Otherwise, there is no any extra perturbation but only noise.

Fig. 1. Optical fiber perturbation sensor, MMF: Multimode fiber When the fiber is exposed to external stimuli, the speckle intensity changes. The CCD captures the images of the speckle, which is transmitted by a MMF. Using image processing techniques, these images are processed by a personal computer (PC) using Matlab. Fig. 2 shows how the NIP changes with respect to bending radius.

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The process to obtain automatically the most significant block and the number of blocks is defined by the Algorithm 1. By this way, system can decide if the perturbation that occurs is real one. In Algorithm 1, the LoG filter has been suggested to extend the dynamic range. The LoG filter can increase the and of normal and abnormal state difference between images, thus extra perturbation can be sensed which means an increase in the dynamic range. In order to divide the image into blocks, the image should be resized first. is a constant, for example, it can be 2,4,8,16 or 32. Algorithm 1. 1. 2. 3. 4. 5. 6. 7.

8.

RGB to gray level Applying LoG filter Resizing the image Dividing the image into number of blocks Comparing every block in the current state with its peer in the previous state Choosing the most significant block then calculating the number of the accepted blocks If the number of the accepted blocks is greater than a definite ratio of the overall number of the blocks (for example half or one third of the overall blocks has go to 8. If not, make the current state the previous state, i.e. if is defined as the current state, go back to the previous state and define this state by . In other words make . Then go to 5. End

Fig. 3. LoG along x and y IV. EXPERIMENTAL SETUP Fig. 4 shows the block diagram of the setup that was done. The laser source has 625 nm wavelength. An attenuator was used because of the short distance of the fiber cable. The fiber cable with 2-meter-long was immersed in the water bulk and the water was heated by an electrical heater. The CCD camera that was used has the following specification: 20.7 MP, auto/manual focus, 10x optical zoom (24-240mm), OIS, Xenon & LED flash. The speckles that were obtained at the output side of the fiber cable are shown in Fig.5 (normal state) and Fig. 6. (abnormal state) These images are transmitted by a router to keep the room closed and dark. Then these images are processed using Matlab.

The Laplacian filter is usually used to detect edges or rapid changes in image. However, The Gaussian filter is used to smooth the image and is mathematically defined by ).

(6)

Fig. 4. Optical fiber temperature sensor, MMF: Multimode fiber.

These two steps compose a LoG filter. By substituting, the LoG is obtained and it is equal to H(L =

).

(6)

Fig 3 shows the frequency respone of the LoG filter.

Fig. 5. Speckle image that is related to the normal state; no heating is done.

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Fig. 8. Gray level speckle shape (double form) after applying LoG filter of a (a): normal state (b): perturbed state.

Fig. 6. Speckle image that is related to a heated fiber.

Table 1 shows how different parameters varies when using LoG filter and 2D DWT filter. represents the number of the blocks that the image is divided into. has the following values: 256, 64, 32 and 4. For every value of , intensity difference, time taken and the number of the accepted blocks is mentioned for both LoG and wavelet filter. The normal state (no perturbation) is compared with the abnormal state 1, which happens when the fiber cable is perturbed with the first perturbation. The difference between the normal state (previous state) and abnormal state 1 (current state) is obtained and represented in the Table 1 as (normal state abnormal state 1). When the fiber cable is perturbed for the second time, the abnormal state 2 (current state) is obtained and compared with the previous state (abnormal state 1). The corresponding row is represented by (abnormal state 1 abnormal state 2).

V. RESULTS Fig. 5 shows the speckle that is was obtained when there is no perturbation. However, when any perturbation is done for example heating the fiber cable, the speckle shape and intensity, are changed. The difference between the intensities of normal and abnormal state images increases when LoG filter is used. For example, the mean of the difference between the most significant block of the normal state image and the peer block of the heated state image was 4.0556 when the 2D DWT filter is used [10]. This difference becomes 14.6191when the filter was added. This big difference can increase the perturbation range. Besides, it increases the accuracy of the system because easily it can be distinguished between the normal state image and perturbed state image since the difference becomes very big. Fig. 7 and Fig. 8 show the speckles of a normal and an abnormal state when using the 2D DWT and LoG filters respectively.

Table 1. Intensity difference, time taken and number of accepted blocks are shown for different values of number of blocks (K). Both (normal state - abnormal state 1) and (abnormal state 1 abnormal state 2) differences are considered.

Another advantage is that the time taken to run the Matlab code of the LoG filter is 0.562 s. This time include reading image and applying the filter and the corresponding analysis. On the other hand, the Matlab code of the 2D DWT filter takes approximately 0.441 s. Therefore, the time taken is decreased by an about 24.5 % when the the LoG filter is used. The computer that has been used has the following specifications: 8 GB installed memory (RAM), an Intel core (TM) i7-3537 U CPU @ 2.00 GHz 2.00 GHz processor (R). The blocks of the normal state image that have different value from the blocks of the abnormal state image was defined as accepted blocks. This number is 64 of 64 in the case of LoG filter and 62 of 64 when the 2D WDT filter is used. If we are taking into consider two abnormal states but with difference in the temperature, this number is 36 for the LoG filter and 34 for the 2D WDT one. Of course, increasing the number of the accepted blocks increases the accuracy of the system.

256

64

Fig. 7. Gray level speckle shape (double form) after applying the 2D DWT filter of a (a): normal state (b): perturbed state.

16

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Parameter

Filter

Normal state abnormal state 1

Intensity difference

2D DWT

4.0556

0.6118

LoG

14.6191

1.0124

2D DWT

0.712

0.712

LoG

0.571

0.571

Number of the accepted blocks

2D DWT

210

128

LoG

256

128


2D DWT

2.1517

0.2218

LoG

12.4986

0.3892

2D DWT

0.562

0.562

LoG

0.441

0.441


2D DWT

62

34

LoG

64

32


2D DWT

1.1652

0.100

LoG

5.9588

0.138

Time taken

Time taken

Abnormal state 1 abnormal state 2

2D WDT

0.530

0.530

LoG

0.416

0.416

[4]


2D WDT

16

8

[5]

LoG

16

8


2D WDT

0.3178

0.0337

LoG

1.9531

0.0404

2D WDT

0.521

0.521

LoG

0.430

0.430

2D WDT

4

2

LoG

4

3

Time taken

4

Time taken Number of the accepted blocks

VI.

[6]

[7]

SENSORS, 2014 IEEE. IEEE, 2014. H. Efendioglu, Y. Tulay, & T. Onur, Advanced image processing and artificial intelligence based approaches to fiber optic statistical mode sensor design Proc. SPIE. Vol. 7982. 2011. [9] W.B. -mode sensor for fiber optic vibration Applied optics 28.15 pp. 3166-3176,1989. [10] E. Fujiwara, M. Marques dos Santos, & C. Kenichi Suzuki. Optical fiber specklegram sensor analysis by speckle pattern division Applied Optics 56.6, pp. 1585-1590, 2017. [11] R.C. Luis, M. Lomer, & J. Lopez-Higuera. Fiber specklegram sensors sensitivities at high temperatures International Conference on Optical Fibre Sensors (OFS24). International Society for Optics and Photonics, 2015. [12] H. Alejandro, N. Darío Gómez, & J. [8]

CONCUSION

Using image processing techniques, the speckle intensity change is measured. This speckle represents the output of optical fiber sensor. For improving the dynamic range and accuracy of a specklegram sensor, LoG filter was used in this paper. The intensity difference between normal and abnormal state images was increased from 4.0556 into 14.611 when =265. The time taken to run the Matlab code was decreased by about 24.5 %. The number of the correct blocks was increased by about 22 %, which leads to an increase in the accuracy of the system. The simulation was done using Matlab. A CCD camera, laser source and MMF were used in the setup. In addition, a router was used between the PC and the CCD camera to control the CCD camera remotely and keep the room dark.

Proc. SPIE. Vol. 8785. 2013. [13] J. Huang, et al. "Polymer optical fiber for large strain measurement based on multimode interference." Optics letters 37.20, pp 4308-4310, 2012. Fiber-optic surface temperature [14] F. Mu Sensors 16.8, pp. 1189, 2016. [15] Optics and Lasers in Engineering 50.12 pp. 1726-1730, 2012. [16] Redding, Brandon, Sebastien M. Popoff, and Hui Cao. "All-fiber spectrometer based on speckle pattern reconstruction." Optics express 21.5, pp. 6584-6600, 2013. [17] T. Yu, et al. Identification of hand postures by force myography using an o 24th International Conference on Optical Fibre Sensors. SPIE-INT SOC OPTICAL ENGINEERING, 2015. [18] Q. Sen, et al. "Investigation on sensitivity enhancement for optical fiber speckle sensors." Optics express, 24.10, pp, 10829-10840, 2016.

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