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Denoising method | Typical algorithm | Implementation | Advantages | Disadvantages |
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Denoising method based on filter in the spatial domain | Average filter | The gray value of pixel is replaced by the mean of its neighboring pixels gray values. | The irrelevant details and gaps are removed. | The image is blurred. |
Median filter | The gray value of pixel is replaced by the median of its neighboring pixels gray values. | Remove effectively the salt and pepper noise, speckle noise. | Not applied to the image with many dots, lines, and spires. |
Wiener filter | The minimum mean square error criterion is used to adjust the filter effect. | Remove effectively the Gaussian noises. | Computation is complex. |
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Denoising method based on Gibbs and Hough transform | Gibbs | Markov random field theory. | Remove effectively noise points. | Not applied to irregular interference line. |
Hough transform | The straight line in the image is detected by using the point line duality of image space and Hough parameter space. | Remove effectively interference lines. |
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Denoising method based on morphology | Open operation | First corrosion to expansion. | Smooth contours, cut off narrow lines, and eliminate fine. | The effect of denoising varies with operation mode and the size and shape of structural elements; the experiment needs to be repeated; the adaptability is poor. |
Close operation | First expansion to corrosion. | Smooth contour and fill holes, gaps, and fracture of contour line. |
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Denoising method based on connected component | Connected component | The recursive method is used to find the connected domain to deal with pixel points, and then denoising based on gray features and morphological features of connected domain. | Remove effectively the noise interference, and the original details of the characters are generally not lost. | Need to analyze character’s properties; hard to determine distinguish features. |
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Denoising method based on wavelet transform | Wavelet transform | Find the best mapping of original image in the wavelet transform domain to restore the original image. | Retain more image details. | Complex computation and it needs to adjust relative parameters. |
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