Image Enhancement of Underwater Target Detection by Inhomogeneous Illumination Haiyong Zheng, Bing Zheng, Guangrong Ji, Zhongwen Guo
Yuting Sun
Department of Electronic Engineering Ocean University of China Qingdao, China 266100 Email:
[email protected]
Institute of Oceanographic Instrumentation Shandong Academy of Sciences Qingdao, China 266001
Abstract—For underwater target detection by homogeneous illumination or laser, the strong backscattering is mainly generated at the range close to the detector, yielding low visibility. Therefore, we build the underwater imaging system based on inhomogeneous illumination field, which intensity distribution relates to the attenuation pattern of light transmission with an inverse form. It is shown via pool and sea experiments to be effective in decreasing backscattering within a wide field-ofview (FOV), which improves image contrast. For human viewers and/or further automated image processing, this paper compares image enhancement methods based on histogram, sharpening, as well as partial differential equation (PDE). The experiments show the results of the underwater image enhancement methods.
I. I NTRODUCTION The key point of underwater target detection is to solve the backscattering problem which is generated by the suspended particles during the light transmission, especially in turbid water as well as other turbid media [1]. In order to detect the target within a wide field of view (FOV) and to illuminate the target homogeneously in underwater, usually the angleintensity of light source is evenly distributed, as used in air. The characteristic of such underwater illumination is that in any detection plane the intensity of light field is also evenly distributed, and the intensity attenuates greatly while the detection plane moves avway from the light source. In such an illumination field, the detector will receive the strong backscattering noise generated by the strong illumination light at the close range, while the target is only illuminated by greatly attenuated light at far distance. Thus the visible detection distance depends upon the degree of the backscattering. Therefore, the main subject of underwater target detection is to decrease the backscattering effectively [2][3]. One of the attractive solutions is the use of laser as illumination source. By laser illumination, usually a scanning mode or a way of beam-expansion is used. However, the characteristic of the laser light field is in fact the same as that of general light mentioned above, i.e., evenly distributed intensity at the cross-section of the beam and FOV. To solve the backscattering problem, techniques of synchronous scanning and range-gated have been developed, which can effectively decrease the backscattering and increase the visible distance [4][5][6][7][8]. But the laser approach is based on depressing single-way scattering. Theoretically there exist multi-way scattering during
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light transmission, and a large blind area in laser detection is unavoidable. Another disadvantage of laser detection is the great expenses for increasing the illumination power and simplifying the installation. We build the underwater imaging system based on a new inhomogeneous illumination field, which can decrease the strong backscattering at the range close to detector while keeping relatively high light power for target illumination at far distance. And it is proved effective to decrease the backscattering within a wide field-of-view (FOV) which improves image contrast as the energy visibility is hold the same time by our pool and sea experiments [9]. For underwater target detection, the image processing task is to extract the most meaningful information from underwater images. Therefore, we aim to improve the underwater image contrast, which is an important issue for human viewers and/or further automated image processing. This paper compares image enhancement methods based on histogram, sharpening, as well as partial differential equation (PDE). The experimental results give more detailed information about the underwater image enhancement methods. II. U NDERWATER TARGET DETECTION BY INHOMOGENEOUS ILLUMINATION
A. Theory of inhomogeneous illumination The image quality obtained by an underwater imaging system depends mainly on contrast visibility. Increasing the illumination power results in increased backscattering, therefore this will not improve the image quality. Since most underwater imaging systems use a uniform illumination source, strong backscattering at short distances is inevitable. Therefore, we propose using an inhomogeneous illumination field where the power density is inversely proportional to the light attenuation in water. The intensity at center (0 degree) is more than 10000 times than that at border (positive and negative 45 degree), which is depicted in Figure 1. As illustrated in Figure 2, a concentrated light beam illuminates the area underwater via a reflector. A CCD detector is located at a distance S0 from the axis of rotation of the reflector. The receive axis is parallel to the concentrated light beam axis OO′ . The reflector directs the light beam which passes through the inhomogeneous illumination field to the
intensity(Lux)
density, while larger ranges have a higher power density. The scattering light will be attenuated and thus be too weak to have a significant effect on the receiver. This approach to sensing is independent of the view angle and depth of field. In addition, the detector can cover the entire FOV with no blind spots.
2×105
B. Pool and sea experiments The board (Figure 3(a)) and the pipe (Figure 3(b)) are the targets used to test the underwater imaging system in the pool and sea respectively. The images acquired are shown in Figure 4 and Figure 5.
4×104
200
−45◦
−10◦ Fig. 1.
10◦
45◦ θ
The intensity distribution of light source.
target C. The coordinate system (X, Y, Z) originates at C, and Z is the detection axis M N . Y is vertical to the plane XCZ, and Y CX is the detection plane whose center is the light center. Because the brightness at all angles follows an exponential decaying distribution, the intensity of illumination (illumination field E), in the plane Y CX has an exponential decaying distribution in both the X and Y directions. This system will provide a small-angle, high density illumination power at large distances. The detection axis M N intersects the beam at a short distance D where the light power density is low, as shown in the figure. Y
(a) The target board of pool experi- (b) The target pipe of ments. sea experiments. Fig. 3.
The target board and pipe.
N long distance
C X Z
short distance
(a)
(b)
(c)
(d)
D
CCD receiver O O concentrated M light beam ′
reflector Fig. 4.
The underwater images acquired in the pool.
S0
III. I MAGE ENHANCEMENT Fig. 2.
The inhomogeneous illumination field.
Within the inhomogeneous illumination field, the backscattering light is weak at short ranges because of the low power
The aim of image enhancement is to improve the interpretability or perception of information in images for human viewers, or to provide “better” input for other automated image processing techniques.
(a) Fig. 5.
(b)
(a) Sharpening using the Laplacian (b) Sharpening using the improved method. Laplacian method.
The underwater images acquired in the sea. Fig. 7.
A. Histogram processing Histograms are the basis for numerous spatial domain signal processing techniques [10]. These techniques include histogram equalization and histogram matching for image enhancement. As illustrated in Figure 6(a), the image acquired by inhomogeneous illumination is still blurred because of the scattering background noise. A small area of the image is chosen as the ROI (Region Of Interest), for example as shown in Figure 6(b). This can be considered to be illuminated by homogeneous light. The results of histogram equalization and histogram matching to improve the gray-level detail and dynamic range are shown in Figures 6(c) and 6(d), respectively. This shows that the detail of the scale on the target is much clearer.
(a) Original blurred image.
(b) ROI of original image.
The sharpening experiments.
C. Histogram equalization with PDE PDE-based decomposition is employing the total variation energy to split an original image into the structural part and textural part [11][12][13][14]. Given a real image f , and it is decomposed as f = u + v, where u component is modeled by a function of bounded variation (a cartoon or sketchy approximation of f ) carrying geometric information, while v component is modeled by an oscillatory function representing the texture or noise which are characterized as repeated and meaningful structure of small patterns. For images acquired by our underwater imaging system, we use M-S model to describe the cartoon component as piecewise smooth functions and applies L2 norm to constraint the gradients of non-edge pixels, and the oscillating characteristics of texture functions defined by G space to ensure that the resultant cartoon component contains less texture information. The experimental results show that it’s effective to obtain the cartoon and texture components as well as edge component of the underwater targets by MS-G model [15]. Then we use histogram equalization on the texture component decomposed by MS-G model, the experimental results are shown in Figure 8. By Figure 8(d), it is shown that the details and the contour of target are more clear, although some noise is left. IV. C ONCLUSIONS
(c) Histogram equalization. Fig. 6.
(d) Histogram matching.
The histogram processing experiments.
B. Sharpening enhancement The principle objective of sharpening is to highlight transitions in intensity, which makes the target contours easier to distinguish [10]. First, the Laplacian method was used to sharpen the image in Figure 6(a), and the result is given in Figure 7(a). This shows improved contrast and a more continuous target contour. Then the matrix used for the Laplacian was changed according to the image features, which provides the result shown in Figure 7(b), which has a sharper contour.
Underwater target detection based on inhomogeneous illumination can decreases the backscattering and thus provides high quality images. However, it’s still blurred because the illunimation is inhomogeneous. Therefore, we aim to improve the interpretability or perception of information in images for human viewers and/or further automated image processing by way of histogram processing, sharpening, as well as histogram with PDE decomposition. The experimental results show the effectiveness of the underwater image enhancement methods. R EFERENCES [1] S. Q. Duntley, Light in the Sea. Journal of the Optical Society of America, 1963, 53(2): 214-233. [2] J. Kong and B. Zhang, The Review of Underwater Laser Imaging Technology and its Development. Optoelctronic Technology, 2006, 26(2): 129-130. [3] G. L. Foresti and S. Gentili, A Vision Based System for Object Detection in Underwater Images. International Journal of Pattern Recognition and Artificial Intelligence, 2000, 14(2): 167-188.
(a) Original blurred image.
(b) MS-G cartoon component.
(c) MS-G texture component.
(d) Histogram equalization of MSG texture.
Fig. 8.
The histogram processing experiments.
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