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2024年4月16日发(作者：css3动画滑动效果)
encoding image binarization, character segmentation, character normalization and so on. Here are some key aspects of the
process.
3.1 gray image processing
Images are usually color coded, the actual identification with the image is grayscale,
where the need to convert first color-coded images to grayscale. In the RGB color model, if R = G = B, then color (R, G , B)
indicates a Black white color, in which R = G = B is called the value of gray value, gray level processing is to make the color
of the R , G , B component value equal to the process. Gray-scale processing methods are commonly used weighted
average method, that is,
R = G = B = (W R R + W G G + W B B) / 3
Which, W R , W G , W B are the R, G , B the weight of experimental and theoretical proof, when W R = 0.3, W G = 0.59, W B =
0.11, that is when R = G = B = 0.30R +0.59 G +0.11 B, can be the most reasonable grayscale.
3.2 image enhancement processing
3.2.1 Direct gray-scale transformation
① linear gray level transformation: if the image gray scale is linear, as in the original image f (x, y) gray-scale range of [a, b],
asked the transformed image intensity range of up to [c, d], According to the linear law, the transformed image g (x, y) as:
c y x f a
b c d y x g +--=),(),( (1) ② nonlinear transformation -- log transformation and exponential transformation:When the need to
expand low gray zone, gray zone of high compression used on the log transformation, when the need to expand the use of
high gray area index transformation.
3.2.2 smoothing filter - Noise Reduction
As the noise in the area corresponding to the edge of the image gray value of such rapid change with a larger part is a high
frequency, so the use of low-pass filter (ie, smoothing filter) noise. At the same time can make the image fuzzy smoothing is
beneficial to the larger goal of the extraction prior to removal of the smaller details or to target the small interruption link.
Smoothing noise reduction method is to use the template on the image convolution
operation, linear smoothing filter is the most commonly used template is shown in Figure 2 of the 3 × 3 template, this template
and image in pixels by the following method of
convolution , get smooth image noise reduction.
① I n the figure, roaming the template and the template center and map location of each
pixel overlap;
② t he template on the coefficient multiplied with the template under the corresponding
pixel;
③ a dd all the product;
④ I t will assign the figure corresponds to the template and the center of the pixel.
The most commonly used non-linear smoothing filter is median filter, it will all of the values of the region are sorted according
to size, will be sorted in the middle of the pixel values given to the center pixel. Median filter can effectively remove the
random noise, can get a better visual effect.
3.3 Edge detection coding
Edge is the result of discrete gray value can be used to request the first and second
derivative method to detect. Because the derivative of the edge of a large area, rather than the local derivative of the edge of
the small. As the digital image is discrete, not the
derivative, convolution method can replace the differential with the differential
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