LIC color texture enhancement algorithm for ocean vector field data based on HSV color mapping and cumulative distribution function
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Abstract: Texture-based visualization method is a common method in the visualization of vector field data. Aiming at adding color mapping to the texture of ocean vector field and solving the ambiguity of vector direction in texture image, a new color texture enhancement algorithm based on the Line Integral Convolution (LIC) for the vector field data is proposed, which combines the HSV color mapping and cumulative distribution function calculation of vector field data. This algorithm can be summarized as follows: firstly, the vector field data is convoluted twice by line integration to get the gray texture image. Secondly, the method of mapping vector data to each component of the HSV color space is established. And then, the vector field data is mapped into HSV color space and converted from HSV to RGB values to get the color image. Thirdly, the cumulative distribution function of the RGB color components of the gray texture image and the color image is constructed to enhance the gray texture and RGB color values. Finally, both the gray texture image and the color image are fused to get the color texture. The experimental results show that the proposed LIC color texture enhancement algorithm is capable of generating a better display of vector field data. Furthermore, the ambiguity of vector direction in the texture images is solved and the direction information of the vector field is expressed more accurately.
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Figure 1. Procedure of LIC algorithm. Blue line: the streamline generated by positive and negative integrals. Red points: white noise.
Figure 2. Streamline generation in LIC. Red line denote the tangent direction of the streamline.
Figure 11. LIC texture merge with HSV color mapping and cumulative distribution function.
Figure 12. Texture-based visualization combined with a color legend. a. Enhanced LIC color texture, and b. color direction legend. The yellow arrows in a represent the vector field directions of different box areas.
Figure 13. Data set A: 0.5-degree wind data LIC texture color enhancement process and comparison. a. Streamline texture generated by once LIC, b. Streamline texture generated by twice LIC, c. wind direction and velocity map of HSV color mapping, d. texture enhancement results of HSV color mapping and cumulative distribution function (our method), and e. texture enhancement results of linear RGB color mapping and cumulative distribution function.
Figure 14. Data set B: 0.25-degree wind data LIC texture color enhancement process and comparison. a. Streamline texture generated by once LIC, b. streamline texture generated by twice LIC, c. wind direction and velocity map of HSV color mapping, d. texture enhancement results of HSV color mapping and cumulative distribution function (our method), and e. texture enhancement results of linear RGB color mapping and cumulative distribution function.
Figure 16. Texture enhanced visualization of the global ocean wind field. a and b are ocean wind field data collected at two different times.
Table 1. Comparison of running time of different LIC texture color enhancement algorithms in two datasets
Attribute and running time Data set A Data set B Data description 0.5-degree wind data 0.25-degree wind data Data size 721×361 1441×721 HSV color mapping time/ms 86 336 Once LIC/ms 698 2817 Twice LIC/ms 1393 5536 Linear RGB color mapping
and cumulative/ms3053 10849 HSV color mapping and cumulative distribution/ms 3273 11669 
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