A new river flow measurement method based on graphic process has been proposed recently, which gets the velocity in optical imaging modality through measuring the continuous displacement of floating debris, then reconstructs a two-dimensional river surface velocity field by using the velocity of floating debris, and computes the section flow at last. However, the surface optical images have not only lights of target information, but also surface optical noise. It is difficult for reliable and stable continuous displacement detection of complex small observation target, which occupies only a small number of pixels comparing to a large field imaging area and has complex optical reflection properties. To solve this problem, this paper presents a background suppression method based on soft morphology and Retinex theory. Soft morphology is firstly used for the opening operation of the image, and then Retinex theory is used for optimal estimation of image incident component to suppress background of image. Finally, the simulations show that our method is superior to gray morphology and soft morphology on the performance of targets enhancement, noise filtering, and background suppression, and it has better background and targets discrimination quality subjective evaluation and higher signal-to-clutter ratio.
Image background suppression is the method of removing the relatively motionless background information from video frames and retaining the moving target information of a scene from video frames. Background suppression is widely used in image processing applications and is usually the first stage of the detection applications.
In the late 1990s, Fujita used particle image velocimetry (LSPIV) to observe floods in the Yodo River, which has always been used to put artificial tracer particles to the sink in laboratory conditions. The river flow measure method needs to capture images on the river bank of a tilt angle of the surface and measures large areas of natural rivers, gets the velocity in optical imaging modality through measuring the continuous displacement of floating debris such as tree branches, leaves, and other water tracers, then reconstructs a two-dimensional river surface velocity field by using the velocity of floating debris, and computes the section flow by velocity-area method at last. This measurement method was called large-scale particle image velocimetry [
The existing LSPIV method only follows the particle image enhancing technology of traditional PIV [
This paper will propose one new background suppression method of the river surface image based on soft morphology and optimal estimation of image incidence component in Retinex theory. The related works and main contributions of the current work are presented in Section
Morphology was applied in background suppression several years ago, and the background suppression method based on mathematical morphology has been proposed a long time ago. The mathematical morphology method is developed from geometry and proposed by Serra and Soille [
Soft morphology [
This paper will propose a new image background suppression method based on soft morphology. The complex river surface image was processed with soft morphology operation, and then the optimal estimation of image incident component to the result image was made. The results are also compared with two kinds of existing morphological background suppression methods.
Soft morphology is developed from basic gray morphology. Gray morphology filtering method is usually used in the infrared image process. Now, we analyze the effect of the gray morphology filtering method in the river image background suppression.
Gray morphology is the basis of traditional mathematical morphology. The most typical background suppression method of grayscale image is the top-hat transform [
The first step of the process of
Figure
WTH and BTH transform results and gray distribution images with 3 × 3 pixels.
From the comparison of gray distribution images after
WTH and BTH transform results and gray distribution images with 7 × 7 pixels.
In soft morphology, the maximum and minimum operation of standard gray morphology are instead of sorting weighted statistical operation. The determination of weighted coefficients is associated with the structuring element. Unlike standard mathematical morphology, the structuring element
Schematic diagram of soft morphology structuring element
Similar to the standard mathematical morphology, soft morphology erosion and dilation operations are defined as follows:
Formula (
Based on erosion and dilation operation, the definition of soft morphology opening and closing operation is as follows:
Formula (
Figure
Result image of soft morphology operation.
Original image
Result of soft morphology opening operation
Result of soft morphology background suppression
Based on the analysis in the previous section, it can be found that the soft morphology operation has a good performance in suppressing background of the surface image. In order to enhance the image contrast and improve SCR in the image, we make the background suppression of displacement measurement of targets and motion vector estimation in the surface image with the Retinex theory.
Retinex is derived from the combination of retina and cortex. It is a color theory which can describe the color constancy of the human visual system. In the study of the principles of the human visual perception system and psychophysical brightness, Land [
Figure
Schematics of Retinex.
The illumination intensity
Usually, we cannot achieve the reflection luminance
This is also equivalent to the concept of background suppression principle: the high-frequency part (including target and high-frequency noise) can be separated by comparing the original image with low-frequency part of the image. Therefore, how to estimate the light intensity is the key of the issue.
Ferwerda et al. [
The commonly used methods to estimate the incident component include look-up table and convolution methods. To deal with the background suppression issue of the river water visual image, it needs to face multiple different images. Apparently, building a single gray look-up table cannot meet the requirements. Therefore, we use the method of the convolution operation to estimate the optimal incident component. In this method, selecting the appropriate kernel function to do the convolution operation is the key of the problem. Gaussian kernel function can highlight the center position of weight value. Meanwhile, the influence of the surrounding points of the center position can be taken into account. And the estimated image has a good correlation with the original image. Based on the above reasons, 3 × 3 Gaussian kernel function is chosen to do the optimal estimation of the incident component. The values of 3 × 3 Gaussian kernel function are as follows:
The convolution operation to the image with Gaussian kernel function is equivalent to doing a low pass filter. A new image will be achieved after each convolution and the optimal estimated value of incident component can be achieved. According to the literature [
In the process of target motion vector estimation with the river visible background image, the obtained river image has a complex background which includes many lights such as direct illumination from the sun, atmospheric scattering light, surface reflected light (flare), surface-emitting light (reflection), and target reflected light. Therefore, the image will present the uneven light, undulating background, and unidentified target. For the complex situation, a method based on soft morphology and Retinex theory is proposed to realize image background suppression.
According to the previous analysis, we can find that the estimation of incident component can achieve an optimal estimation for low-frequency part of the image. It has important practical significance of the surface visible background image with complex lighting conditions. Through the soft morphology operations and optimal incident component estimation, we can achieve the optimal estimation of the background image. Then, by using the original image to subtract the estimated image, a background suppression image with a higher signal-to-noise ratio will be achieved. The flowchart of the proposed method is shown in Figure
Background suppression flowchart based on soft morphology and Retinex theory.
It has been shown that the optimized size of operator structure is generally equal to the half of the maximal size of a small two-dimensional target [
The structural element
According to the aforementioned method of optimal estimation of image incident component, we make a cubic convolution with the result after Step
Using the original image to subtract the optimal estimated image obtained after Step
In order to verify the effectiveness of the proposed method, we make an experiment with the method which combines with soft morphological opening operation and optimal estimation of incident component with background suppression of the image based on Retinex theory. We make a comparison between
Comparison of three methods.
Figures
Comparison of three background suppression methods.
Original image | WTH transform | SWTH transform | Our method | ||||
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Indicators |
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Img. 1 | 16.8 | 36.38 | 15.45 | 39.23 | 16.96 | 42.2 | 18.42 |
Img. 2 | 23.29 | 37.12 | 9.32 | 41.37 | 11.49 | 49.64 | 15.14 |
Img. 3 | 15.90 | 33.55 | 14.93 | 35.17 | 15.87 | 35.71 | 16.19 |
Comparison of three methods on 3D grayscale distribution diagrams.
Compared with the three methods mentioned in this paper, the proposed background suppression method has a notable improvement of SCR in the image, and the SCR Gain is also the largest. Grayscale morphology WTH transform has a weak performance in background suppression, and the soft morphology SWTH has a medium performance in background suppression. Therefore, the experiment fully demonstrated that the proposed method has a better ability in background suppression than the other two methods. However, this method also has some shortcomings. After the image processing of image 3, the effect of background suppression has no notable improvement compared with WTH and SWTH, and the three methods also have almost the same performance in SCR Gain. The reason is that the pixel size of the target in image 3 is large. In this method, the large size target with soft morphological opening operation cannot achieve an ideal result in background clutter residual. Therefore, the background image has a big fluctuation after background suppression. Although the improvement of SCR is not notable, the target grayscale and image contrast have a notable improvement. Moreover, our method has the largest grayscale value and optimal target visibility among the three methods, and the subjective evaluation of the quality in discrimination between the background and target is also the best.
To overcome the shortcomings of surface noise and clutter, surface tracer optical reflection complexity, difficulty in target displacement detection, and motion vector estimation, we present a background suppression method based on soft morphological filtering and Retinex theory in this paper. In order to improve the performance of surface image background suppression method, we use the Retinex theory and make an optimal estimation of incident component of the background image through soft morphological opening operation. The experiments give the results of background suppression of surface image and make a comparison with grayscale morphological WTH transform and soft morphology SWTH transform experiments. The simulations show that the proposed method has a notable improvement in background suppression of surface image. Meanwhile, our method makes a good preparatory work for the next target displacement detection and motion vector estimation.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This paper is partially supported by the National Natural Science Foundation of China (no. 61263029, no. 61374019), a project funded by the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions, and Natural Science Foundation of Jiangsu Province (no. BK20130851).