ALGORITHM OF PRELIMINARY SESSILE DROP PROCESSING FOR NOISE SUPPRESSION X

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1 ALGORITHM OF PRELIMINARY SESSILE DROP PROCESSING FOR NOISE SUPPRESSION X Y.P. KULYNYCH, L.I. MURAVSKY, R.S. BATCHEVSKY, O.G. KUTS Physic-Mechanical Institute of the National Academy of Sciences of Ukraine, depart. 4; 5 Naukova St., Lviv 9060, Ukraine; dep4@ah.ipm.lviv.ua ABSTRACT The definition of the high-temperature melts, alloys and other substances capillary characteristics by the sessile drop method is based on finding the exact localisation of the sessile drop image edges for precise measurement the sessile drop geometric parameters. The use of the matrix videosensors for sessile drop image recording allows to improve the measurement accuracy of a drop [,]. Therefore, hybrid optical/digital systems containing the CCD-camera are successfully used for exact definition of capillary characteristics [-4]. The principal sources of the sessile drop geometric parameters measuring errors are caused by the spatial-temporal noise and the spatial (geometric) noise emerged in the optical, optoelectronic and electronic channels of these systems [4]. The high-frequency constituents of these noises form the impulse disturbances in a sessile drop image whose suppression by the traditional methods of digital image processing involve the elimination of the image contours parts, essentially the low-contrast contours. In this paper we analysed the noises influenced on the sessile drop image quality in the hybrid optical/digital system (HODS). We have proposed the experimental method of the noise measurement in the HODS. We have measured the temporal and spatial variances of spatialtemporal and spatial noises and have found the experimental relationships between the variances of noises and relative intensities of the sessile drop image pixels. We also have proposed the new algorithm for high-frequency impulse noise suppression which allows to select the shadow (halfshadow) sessile drop image and to suppress this noise completely. ANALYSIS OF NOISES IN THE HYBRID OPTICAL/DIGITAL SYSTEM The HODS experimental setup was used for capillary characteristics measurement and for noise evaluation. This setup included the light illuminated source (photonemitting diode with the maximum intensity radiation s light length λ =0.67 µm), the collimator lens, the sessile drop inside the pressure cell simulator, the projective lens, the CCD-camera MTV-S0CB included the CCD-array with 5x5 spatial elementary photosensors (pixels), the frame- grabber and the personal computer. The optical noises emerged in the optical and optoelectronic parts of the HODS include the spatialtemporal noise of the lighting background emerged by the light source and surrounded the shadow or self-shadow sessile drop image, the noise of the pressure cell s area, the noise of the pressure cell s optical windows, the noise of the projective lens, the geometric noise of the CCD-array. The noises of the optoelectronic part include the temporal noise, the geometric noise of the CCD-camera and the frame-grabber noise. Besides, the sessile drop random lateral oscillations can be the significant source of the Proc. Int. Conf. HIGH TEMPERATURE CAPILLARITY 9 June July 997, Cracow, Poland Edited by N. Eustathopoulos and N. Sobczak

2 4 Y.P. KULYNYCH, L.I. MURAVSKY, R.S. BATCHEVSKY, O.G. KUTS spatial-temporal noise. Therefore we considered the noise of the sessile drop random lateral oscillations (RLO-noise) as independent noise. Thus, the sessile drop image noise we divided in the next principal types: the spatial-temporal noise, the geometric noise and the RLO-noise. As usually, the RLO-noise is caused by mechanical vibrations which are the source of the random oscillations of the sessile drop image edges. For estimation of the RLOnoise we fulfilled the temporal averaging of the relative intensities of the edges pixels in the sessile drop images series. Each series of K frames (30-35 frames) contained the same sessile drop images. The pixels temporal averaging was realised for the selected pixels relative intensities at the drop pole spatial region (region ) and at the spatial region of the drop s equator intersection with the. drop edge (region ). At the region we studied the pixels columns and at the region we studied the pixels rows. The most informative pixels were located at the image edges. The relative intensities of these pixels were utilised as samples of the frame series for the RLO-noise estimation. The temporal variance of each pixel s relative intensity in the n-th column at the region was defined as K [ ( bi', j= n) ( bi', j= n) ] σ ( b () i', j= n) = K k= and the temporal variance of each pixel s relative intensity in the m-th row at the region was defined as K [ ( bi= m, j' ) ( bi= m, j' ) ] σ ( b () i= m, j' ) = K k= where k =,..., K is the number of a frame, i = m, m +,..., m -, m is the pixel number in the n-th frame row s part limited by the region, i =,..., m,..., m,..., M is the pixel number in the n-th frame row, M is a pixel quantity in a frame row, j = n, n +,..., n -, n is the pixel number in the m-th frame column s part limited by the region, j =,..., n,..., n,..., N is the pixel number in the m-th frame column, N is a pixel quantity in a frame column. For statistical estimation of the spatial-temporal and geometric noise we recorded R series of K frames in each series without the sessile drop images. Each series contained the frames with the same even level of illumination. These frames described by the relative intensity b, where r =,,...,R. We selected forty columns with forty pixels in each column at the regions of each frame belonged to one of the R frame series. We also selected forty rows with forty pixels in each row at the regions of each frame belonged to one of the R frame series. The temporal variances of the pixels relative intensities for each frame series were defined from Eq. () and from Eq. (). The spatial variance of each pixel relative intensity in the p-th column of each frame series was defined as σ [( br ) p ] = [( br ) p, s ( br ) p ] (3) s=

3 ALGORITHM OF PRELIMINARY SESSILE DROP PROCESSING... 5 and the spatial variance of each pixel relative intensity in the q-th row of each frame series was defined as σ [( br ) q ] = [( br ) q, t ( br ) q ] (4) s= We also recorded the two frame series of the same straight edge to estimate the spatial- temporal noises in the whole dynamic range of the pixels intensities. This edge was placed at the region in the horizontal direction for the first frame series and at the region in the vertical direction for the second frame series. Evaluation of the CCDarray geometric noise was fulfilled by averaging the relative intensity variances of each pixel at the regions and. The experimental results of the noise variances measurement at the region are presented in Fig.. The spatial-temporal noise variances σ (b i ) are presented in Fig. as ten sets (R=0, b= b r b r- = 0.) of the points dispersed within ten levels of the even relative illuminations of the frame series. These variances are marked by number in Fig.. The geometric noise variances σ p (b r ) are reflected in Fig. by the broken line connecting the points of the spatial variances mean values σ p ( b r ). These variances are marked by number in Fig.. The spatial-temporal noise variances σ h.e.(b i ) of the horizontal straight edge images were calculated for the pixel columns cut the edge placed at the region. These variances are represented by the set of points within the band marked by number 3 in Fig.. Note that the noise () and the noise (3) in Fig. are nearly coincided. Analogous results were obtained at the region. The spatial-temporal noise variances σ (b j ), the spatial noise variances σ q ( b r ) and the spatial-temporal noise variances σ v.e.(b j ) calculated for the second frame series pixels rows intersected the images of the edges at the region are represented in Fig. (numbers, and 3 respectively). Note that each pair of the noise variances represented in Fig. and Fig. (numbers, and 3 respectively) is arbitrary equal. The experimental RLO-noise study of the distilled water sessile drop have shown that the distributions of the RLO-noise temporal variances in the whole dynamic range of a pixel intensities at the regions and are dispersed in the limits of the convex bands. The mean variance W of the convex band maximum for the region equals to 4.8x0 - and the mean variance σ W of the convex band maximum for the region equals to 9.6x0-3. The bands maximums are reached at the b = 0.5, because the position of the drop edge with such relative intensity level in the drop image is corresponded to the drop edge position at the HODS input. Analogous results were obtained for the mercury sessile drop. For such drop the mean variance σ M of the convex band maximum for the region equals to.4x0-3 and the mean variance σ M of the convex band maximum for the region equals to.4x0-3. Thus, it is necessary to give special attention to decrease the RLO-noise because this noise can significantly exceed the all other noises.

4 6 Y.P. KULYNYCH, L.I. MURAVSKY, R.S. BATCHEVSKY, O.G. KUTS ALGORITHM OF IMPULSE NOISE SUPPRESSION The sessile drop image can be considered as the shadow or the half-shadow image surrounded by the noises examined above. These noises are superimposed and the spatial disturbances as a result of noise superposition are formed around the shadow (halfshadow) drop image. These disturbances can be considered as the image noise. The random spots with small squares surrounding the shadow drop are also the image noise. These spots can be considered as the spatial impulse noise or so-called salt-and-pepper noise. The algorithms based on the discrete approximation of the first and the second degree differential operators are widely used for an image edge extraction [5,6]. But their direct application is difficult because the image noise is essentially influenced on the image processing product. Such noise contains the high-frequency terms and the sessile drop image differentiation significantly amplifies these terms. Therefore, the signal-to-noise ratio has fallen significantly after image processing. The algorithm for the impulse noise suppression and the sessile drop image selection based on the Laplacian discrete approximation and application of the smoothing spatial filter with a Gaussian transfer function was proposed in [5]. But this algorithm is applicable only for such images, whose Fourier transforms are matched with a Gaussian spatial filter. Fig.. The noise variances distributions at the region. Fig.. The noise variances distributions. at the region. We propose the new algorithm for the impulse noise suppression and the sessile drop image processing. Next conditions must be fulfilled in the process of this algorithm realisation: (a) an impulse noise is represented as small spots, and squares of such spots are significantly smaller than a drop square; (b) a large light spot or a few light spots at a central region of the shadow drop image also must be suppressed by this algorithm. The process of the algorithm realisation can be separated to the four stages. As the first stage we realised the adaptive binarization of the input sessile drop image. As a result we obtain the image included the definite quantity of spots. Evidently, the square corresponded with the shadow drop image is significantly larger than all other squares of the spots represented the impulse noise. As the second stage we carry out the marking of the selected spots to separate the image pixels of one spot from the pixels of other spot. On this stage we apply the successive marking algorithm described in [6]. After marking we find the squares of the selected spots. The third stage is aimed on the finding of the spots corresponding to the impulse noise and the correcting of the impulse noise s spatial samples. In order to fulfil this stage, we choose the threshold magnitude as a

5 ALGORITHM OF PRELIMINARY SESSILE DROP PROCESSING... 7 certain reference square. If an object s square is smaller than chosen threshold, the object is assumed as a noise. As the last stage we remove the light spots at the central region of the shadow drop image by marking the inverse image and repeating the threshold comparison with the reference square. As a result, we obtain the sessile drop image without the removed impulse noise and the light spots at the central region of the shadow drop image. The obtained image is prepared for the next stage of the automatic image processing, in particular, for the sessile drop geometric parameters precision measurement and the capillary characteristics definition. The input distilled water sessile drop image recorded in the HODS experimental setup by the CCD-camera MTV-80CB is represented in Fig. 3. The sessile drop image obtained after processing of the input image with proposed algorithm is represented in Fig. 4. Obtained image is adaptively binarized and cleared from noises. Such image can be applied for direct precision measurement of the sessile drop geometric parameters. Fig. 3. The input distilled water sessile drop image. Fig. 4. The distilled water sessile drop image, obtained as the result of the impulse noise suppression algorithm realisation. CONCLUSIONS In this paper we proposed the methods of the sessile drop image noises evaluation and suppression. The method for spatial-temporal noise, geometric noise and RLO-noise estimation is developed for HODS contained the CCD-camera. The experimental study of the noise and its variances distributions calculations based on the proposed method can be repeated in the same manner for any other automatic system included the CCD-array or other discrete videosensor. Proposed algorithm of the sessile drop image processing is performable for high-frequency image noise suppression. This algorithm can be applicable not only for sessile drop images but for other images containing impulse noises. REFERENCES. L. Liggieri and A. Passerone, High Temp. Technol. 7, 8 (989).. P. Cheng, D. Li, L. Boruvka, J. Rotenberg and A.W. Newman. Colloids and Surfaces. 43, 5 (990). 3. R.S. Bachevsky, V.A. Dostojny, L.I. Muravsky, A.I. Stefansky, Y.V. Naidich and M.F. Grygorenko. Proc. SPIE. 3, 6 (994). 4. R.S. Bachevsky, Y.V. Naidich, M.F. Grygorenko, V.A. Dostojny, L.I. Muravsky and A.I. Stefansky in Proc. Int. Conf. High Temperature Capillarity, Smolenice Castle, May 994, edited by N. Eustathopulos (Reproprint, Bratislava, 995)) pp D. Marr, E. Hildreth. Proc. of the Royal Society of London B. 07, 87 (9SO).

6 8 Y.P. KULYNYCH, L.I. MURAVSKY, R.S. BATCHEVSKY, O.G. KUTS 6. B.K.P. Horn, Robot Vision, (The MIT Press, Cambridge, Massachusetts; London, England; McGraw-Hill Book Company, New York, St. Luis, San Francisco, Montreal, Toronto, 9S6), p. 4S7.