# Underwater 2D Image Acquisition Using Sequential Striping Illumination
## 4 Experimental Results  
Following the descriptions in Sections 2 and 3, the steps of the system implementation are described below:  
(i) Project the designed illumination pattern and record the frame image synchronously;  
(ii) Complete the scanning process by sequentially translating the illumination pattern with 1-pixel displacement at the projector and obtain the sequential frame images accordingly;  
(iii) Apply the virtual aperture to each frame image to remove back scattered light;  
(iv) Complete the convolutional integration by integrating each frame image in one dimension, i.e., to sum up the row of pixel values to output a single pixel value, and one frame image outputs one column of the image _g(s)_ of Equation (5); all frame images give rise to the complete _g(s)_;  
(v) Apply the deconvolution routine to recover the albedo _O(x)_ of Equation (5); the initial estimate of _I(x)_ is given by the designed illumination pattern. Figure 4: Determination of window mask. (**a**) Transverse intensity distribution of the projecting stripe light and the preset threshold of the window. (**b**) Single frame image obtained underwater. (**c**) Single frame image after applying smoothing filter to (**b**). Along the dotted line, the starting and ending points of the mask are determined accordingly with respect to this line. (**d**) Frame image after the removal of back scattered light. (**e**) Gray level distribution along the \(x\) direction (dotted line) before and after the removal of backscattering. Figure 5: Sample synthesized image. (**a**) Result before the removal of back scattered light. (**b**) Result after the removal of back scattered light. In our experiments, objects were placed in a \(150\times 150\times 300\) cm\({}^{3}\) glass tank. The scattering medium was emulated using water mixed with different quantities of milk and aluminum hydroxide. The CCD video camera is a GO-5000-PGE camera with maximum resolution of 2560*2048 and a maximum frame rate of 22. The projector is an EPSON CH-TW6700W with 1.6x zoom lens and resolution of 1080 p (\(1920\times 1080\)). The lamp brightness is 3000 lumens. A CCD video camera and a DLP projector were placed outside the tank. The experimental setup and examples of the frame image in water of varying turbidity are shown in Figure 6.  
The experimental results were obtained following the implementation steps as listed above. For image recovery, the well-developed blind deconvolution algorithm of Lucy-Richardson method (L-R) was applied [15; 16; 17]. The results of image recovery with light turbidity of water are shown in Figure 7. Considering the wavelength-dependent transmission in underwater imaging, we also applied the algorithm to RGB channels, and the colored versions of image recovery are given below the gray-scale versions. Figure 8 shows the results of image recovery with heavy turbidity of water. Figure 6: **(a)** Experimental setup; **(b)** Scanning image with light turbidity water; **(c)** Scanning image with heavy turbidity water. Compared to the floodlit image, the recovered images using the proposed method have a significantly improved image contrast, even with heavy turbidity of the water. The improvement  
Figure 8: Experimental results of image recovery with heavy turbidity of water. (**a**) Floodlit gray level image; (**b**) Synthesized image after the removal of the back scattered light; (**c**) Result of blind deconvolution using the L–R method; (**d–f**) Colored versions associated with the same implementations as of (**a–c**) with respect to RGB channels, respectively. Figure 7: Experimental results of image recovery with light turbidity of water. (**a**) Floodlit gray level image; (**b**) Synthesized image after the removal of back scattered light; (**c**) Result of blind deconvolution using the Lucy–Richardson (L–R) method; (**d–f**) Colored versions associated with the same implementations as of (**a–c**) with respect to RGB channels, respectively.