CN102622597A - Self-adaptive orthogonal median hybrid filtering method - Google Patents
Self-adaptive orthogonal median hybrid filtering method Download PDFInfo
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- CN102622597A CN102622597A CN2011100314772A CN201110031477A CN102622597A CN 102622597 A CN102622597 A CN 102622597A CN 2011100314772 A CN2011100314772 A CN 2011100314772A CN 201110031477 A CN201110031477 A CN 201110031477A CN 102622597 A CN102622597 A CN 102622597A
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Abstract
A self-adaptive orthogonal median hybrid filtering method belonging to image recognition technical field is provided. The aim of the self-adaptive orthogonal median hybrid filtering method provided by the invention is to keep more details of an original image and simultaneously well remove image noise. The method comprises the steps of taking the center pixel as the core, taking out the rest four pixel points respectively in two orthogonal directions, dividing them into two groups: pixel group of group I and pixel group of group II, and carrying out a same treatment on each pixel point of the image to get a smoothly filtered image. The method is mainly used for smoothing an image during an image recognition process andis able to keep more details of the image and simultaneously well remove image noise during an image filtering process.
Description
Technical field
The invention belongs to the image recognition technique field.
Background technology
Existing filtering algorithm has also kept picture noise when keeping image detail, be unfavorable for the identification and the judgement of image detail, and existing filtering algorithm is considered, and image detail also is removed or obfuscation when removing noise.
Summary of the invention
The object of the invention has been removed the adaptive quadrature intermediate value mixed filtering method of picture noise again well when keeping original image more details.
Step of the present invention is:
A, be that core is taken out all the other four pixels at two orthogonal directionss respectively with the center pixel, be divided into into two groups: I group pixel groups is organized pixel groups with II;
B, condition one: the I class mean is less than the maximal value of five pixel gray-scale values in the I group, and greater than the minimum value of five pixel gray-scale values of I group;
C, condition two: the II class mean is less than the maximal value of five pixel gray-scale values in the II group, and greater than the minimum value of five pixel gray-scale values of II group;
If d condition one satisfies, condition two does not satisfy, and the gray-scale value of the center pixel of nine pixels of getting is the intermediate value of I group with regard to value;
If e condition two satisfies, condition one does not satisfy, and the gray-scale value of the center pixel of nine pixels of getting is the intermediate value of II group with regard to value;
If f condition one and condition two do not satisfy or condition one all satisfies with condition two, the gray-scale value of the center pixel of nine pixels of getting is the mean value of I group and II class mean with regard to value;
G, each pixel of picture handled equally after, will obtain an image behind the smothing filtering.
The present invention is mainly used in the smoothing processing of image in the image recognition processing process.Remove picture noise better when can in the image filtering process, keep the image more details.
Description of drawings
Fig. 1 is an original image;
Fig. 2 adds the salt-pepper noise image;
Fig. 3 is the mean filter image;
Fig. 4 is the adaptive median filter image;
Fig. 5 is the medium filtering image;
Fig. 6 is an adaptive quadrature intermediate value mixed filtering image of the present invention;
Fig. 7 is that the present invention gets quadrature neighbor pixel synoptic diagram anyhow with central pixel point;
Fig. 8 is that the present invention gets quadrature neighbor pixel synoptic diagram with the central pixel point skewed crossing.
Embodiment
Step of the present invention is:
A, be that core is taken out all the other four pixels at two orthogonal directionss respectively with the center pixel, be divided into into two groups: I group pixel groups is organized pixel groups with II;
B, condition one: the I class mean is less than the maximal value of five pixel gray-scale values in the I group, and greater than the minimum value of five pixel gray-scale values of I group;
C, condition two: the II class mean is less than the maximal value of five pixel gray-scale values in the II group, and greater than the minimum value of five pixel gray-scale values of II group;
If d condition one satisfies, condition two does not satisfy, and the gray-scale value of the center pixel of nine pixels of getting is the intermediate value of I group with regard to value;
If e condition two satisfies, condition one does not satisfy, and the gray-scale value of the center pixel of nine pixels of getting is the intermediate value of II group with regard to value;
If f condition one and condition two do not satisfy or condition one all satisfies with condition two, the gray-scale value of the center pixel of nine pixels of getting is the mean value of I group and II class mean with regard to value;
G, each pixel of picture handled equally after, will obtain an image behind the smothing filtering.
Below concrete description is done in invention:
Fig. 7 and nine grids shown in Figure 8 are represented nine adjacent pixels of the left and right sides in a certain picture, and being core with the center pixel takes out all the other four pixels at two orthogonal directionss respectively by shown in the figure, are divided into into Fig. 7 and two groups shown in Figure 8.
In I group pixel groups, average after the gray-scale value summation with five pixels and be defined as the intermediate value of I group pixel.
Condition one: the I class mean is less than the maximal value of five pixel gray-scale values in the I group, and greater than the minimum value of five pixel gray-scale values of I group.
In like manner in II group pixel groups, average after the gray-scale value summation with five pixels and be defined as the intermediate value of II group pixel.
Condition two: the II class mean is less than the maximal value of five pixel gray-scale values in the II group, and greater than the minimum value of five pixel gray-scale values of II group.
If condition one satisfies, condition two does not satisfy, and the gray-scale value of the center pixel of nine pixels of getting is the intermediate value of I group with regard to value.
If condition two satisfies, condition one does not satisfy, and the gray-scale value of the center pixel of nine pixels of getting is the intermediate value of II group with regard to value.
If condition one and condition two do not satisfy or condition one all satisfies with condition two, the gray-scale value of the center pixel of nine pixels of getting is the mean value of I group and II class mean with regard to value.
According to above-mentioned processing mode, after each pixel of picture handled equally, will obtain an image behind the smothing filtering, this method is defined as adaptive quadrature intermediate value mixed filtering method.
represents one will be processed, the Centre
at the sub-image.
With Sxy
is the implementation procedure that example is explained this algorithm.
Extract two groups of pixels
,
by Fig. 7 and mode shown in Figure 8, and do to give a definition:
represents
the minimum luminance value
represents
the maximum luminance value
If Juz1 is true, Juz2 is false
If Juz1 is false, Juz2 is true
Claims (1)
1. adaptive quadrature intermediate value mixed filtering method is characterized in that:
A, be that core is taken out all the other four pixels at two orthogonal directionss respectively with the center pixel, be divided into into two groups: I group pixel groups is organized pixel groups with II;
B, condition one: the I class mean is less than the maximal value of five pixel gray-scale values in the I group, and greater than the minimum value of five pixel gray-scale values of I group;
C, condition two: the II class mean is less than the maximal value of five pixel gray-scale values in the II group, and greater than the minimum value of five pixel gray-scale values of II group;
If d condition one satisfies, condition two does not satisfy, and the gray-scale value of the center pixel of nine pixels of getting is the intermediate value of I group with regard to value;
If e condition two satisfies, condition one does not satisfy, and the gray-scale value of the center pixel of nine pixels of getting is the intermediate value of II group with regard to value;
If f condition one and condition two do not satisfy or condition one all satisfies with condition two, the gray-scale value of the center pixel of nine pixels of getting is the mean value of I group and II class mean with regard to value;
G, each pixel of picture handled equally after, will obtain an image behind the smothing filtering.
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| CN102622597A true CN102622597A (en) | 2012-08-01 |
| CN102622597B CN102622597B (en) | 2016-05-04 |
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Citations (6)
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| CN101094312A (en) * | 2006-06-20 | 2007-12-26 | 西北工业大学 | Self-adapting method for filtering image with edge being retained |
| US20090060330A1 (en) * | 2007-08-30 | 2009-03-05 | Che-Bin Liu | Fast Segmentation of Images |
| CN101388113A (en) * | 2008-10-24 | 2009-03-18 | 北京航空航天大学 | Star map image rapid denoising method |
| CN101425176A (en) * | 2008-12-09 | 2009-05-06 | 中国科学院长春光学精密机械与物理研究所 | Image wavelet de-noising method based on median filter |
| CN101944230A (en) * | 2010-08-31 | 2011-01-12 | 西安电子科技大学 | Multi-scale-based natural image non-local mean noise reduction method |
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2011
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Patent Citations (6)
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| US20050094889A1 (en) * | 2003-10-30 | 2005-05-05 | Samsung Electronics Co., Ltd. | Global and local statistics controlled noise reduction system |
| CN101094312A (en) * | 2006-06-20 | 2007-12-26 | 西北工业大学 | Self-adapting method for filtering image with edge being retained |
| US20090060330A1 (en) * | 2007-08-30 | 2009-03-05 | Che-Bin Liu | Fast Segmentation of Images |
| CN101388113A (en) * | 2008-10-24 | 2009-03-18 | 北京航空航天大学 | Star map image rapid denoising method |
| CN101425176A (en) * | 2008-12-09 | 2009-05-06 | 中国科学院长春光学精密机械与物理研究所 | Image wavelet de-noising method based on median filter |
| CN101944230A (en) * | 2010-08-31 | 2011-01-12 | 西安电子科技大学 | Multi-scale-based natural image non-local mean noise reduction method |
Non-Patent Citations (2)
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| 张旭明等: "用于图像处理的自适应中值滤波", 《计算机辅助设计与图形学学报》 * |
| 肖蕾等: "改进自适应中值滤波的图像去噪", 《激光杂志》 * |
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