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Binary image noise-reduction method based on integral graph and binary image processing system
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A binary image and integral image technology, applied in the field of image processing, can solve problems such as being unfavorable for the calculation of characteristic parameters, and achieve the effect of simple implementation and elimination of spots
Active Publication Date: 2011-04-20
维度信息技术(苏州)有限公司
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Using morphology for noise reduction, the selection of templates and the number of template operations are different for different images, and the morphological method uses a specific structural element group to traverse the entire image without affecting noise and Effective information is treated differently, so in order to completely remove the noise in the noise reduction process, it is easy to remove the effective information in the image, which will not be conducive to the subsequent feature parameter calculation
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Embodiment 1
[0030] The process of binary image noise reduction method is as follows: figure 1 shown.
[0031] S1: Read in pixels point by point from left to right and top to bottom, and calculate the integral image value of each pixel with non-zero gray value, as shown in formula (1):
[0032] sum1(d)=sum1(b)+sum1(c)-sum1(a)+1; (1)
[0033] S2: Read in pixels point by point from right to left and bottom to top, and calculate the integral image value of each pixel with a non-zero gray value, as shown in formula (2):
[0034] sum2(d)=sum2(e)+sum2(f)-sum2(g)+1; (2)
[0035] Such as figure 2 As shown, d represents the current foreground pixel, a represents the adjacent pixel located at the upper left of the current foreground pixel, b represents the adjacent pixel directly above the current foreground pixel, and c represents the directly left of the current foreground pixel adjacent pixels of the current foreground pixel; e represents the adjacent pixel directly to the right of the curre...
Embodiment 2
[0044] In order to make the noise reduction effect more accurate, Embodiment 1 can be extended to improve the calculation of the integral image value, and calculate the integral image value from four directions, that is, from left to right, from top to bottom, from right to left, from bottom to bottom Calculate integral image values in four directions: top, left to right, bottom to top, right to left, top to bottom, and then determine whether the foreground pixel is a noise point by setting a threshold T.
[0045] Such as figure 2 As shown, when judging the current foreground pixel d, not only the adjacent pixels a, b, c, e, f, g in Implementation 1 should be considered, but also the adjacent pixel h on the upper right of the current foreground pixel and The adjacent pixel i below the left of the current foreground pixel.
[0046] The process flow of the binary image noise reduction method is based on the embodiment 1, and it needs to be carried out before step S3:
[004...
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Abstract
The invention relates to an image processing technology, in particular to a binary imagenoise-reduction technology. The invention provides a binary imagenoise-reduction method based on an integral graph, which is used for extracting and deleting noise points to ensure that shape characteristics of target images is not influenced by the noise reduction process, and the invention provides a binary imageprocessingsystem. Through calculating the sum of a plurality of integral graph values of prospect pixel points and comparing the sizes of set thresholds, the noise points are extracted, the implementation is simple and fast, and spots and burrs in the binary image are effectively removed without influencing the shape characteristics of the target image.
Description
technical field [0001] The invention relates to image processing technology, in particular to binary image noise reduction technology. Background technique [0002] Digitized binary images are widely used in engineering, such as pattern recognition and machine vision applications, most of which require the collected images to be binarized for subsequent image analysis. The original image usually has noise. Although many binarization methods have been proposed, the existence of noise in the binarized image cannot be avoided, which will affect the subsequent processing of the image, so it is necessary to denoise the binarized image. [0003] The method of denoising binary images is different from that of true color images and grayscale images, because the noise of binary images is different from salt and pepper noise and Gaussianwhite noise in general grayscale images or true color images. Assuming that the white pixels in the binary image represent the foreground image, and...
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