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@@ -8,7 +8,7 @@ In this paper image smoothing algorithm based on gradient analysis is proposed.
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Regular boundaries have small deviations of gradient angles and the opposite for irregular ones. To measure closeness of angles cosine of doubled difference is used. As additional measure that helps to discriminate the types of boundaries inverted gradient values were used.
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When gradient magnitudes are inverted bigger values refer to textures (insignificant changes in gradient) and smaller refer to strong boundaries. So textures would have bigger weights and hence they would appear smoother. We also propose to smooth image of gradient magnitudes with median smooth to enhance visual quality of results. The method proposed in this paper is easy to implement and compute and it gives good results in comparison with other techniques like bilateral smooth.
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When gradient magnitudes are inverted bigger values refer to textures (insignificant changes in gradient) and smaller refer to strong boundaries. So textures would have bigger weights and hence they would appear smoother. We also propose to smooth image of gradient magnitudes with median filter to enhance visual quality of results. The method proposed in this paper is easy to implement and compute and it gives good results in comparison with other techniques like bilateral filter.
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## Examples
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d) - our filter
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## Edge detection
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Here is the output of Canny edge detector that was applied on the image with and without preprocessing with our smooth.
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Here is the output of Canny edge detector that was applied on the image with and without preprocessing with our filter.
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## How to use code
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cv::Mat img = cv::imread("your_input_file_name", cv::IMREAD_COLOR); //read image using opencv from file into Mat type
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int kernelSize = 3; //set kernelSize = 3 for filtering with 3x3 kernel
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Filter<float, uint8_t> smooth; //create the instance of smooth
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