Bilateral regression filter method for grayscale and colored images

A color image, bilateral technology, applied in image enhancement, image analysis, image data processing, etc., can solve the problems of image edge blur, bilateral filter denoising effect needs to be strengthened, bilateral filter local weighted average failure, etc. strong noise effect

Inactive Publication Date: 2016-11-16
HARBIN UNIV OF SCI & TECH
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Problems solved by technology

When applied to an image containing salt and pepper noise, since the gray value of the noise point is significantly different from that of adjacent points, the local weighted average of the bilateral filter will fail, so the bilateral filter cannot remove the salt and pepper noise.
Durand and Dorsey proposed to combine bilateral filtering and median filtering to filter salt and pepper noise [5] , but the median filter will lose the details of the image
In addition, in order to achieve a good filtering effect, the bilateral filter should set the standard de

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  • Bilateral regression filter method for grayscale and colored images

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Embodiment 1

[0027] A bilateral regression filtering method that can be used for grayscale and color images. For grayscale images, the implementation process of the method includes the following steps:

[0028] (1) This filtering method can be expressed by formula (1) (including one-dimensional bilateral regression filter and two-dimensional bilateral regression filter),

[0029] (1)

[0030] In formula (1) h ( ) represents the spatial weight of the spatial domain kernel function, δ ( ) represents the range weight function (rangeweight), I Indicates the gray level of the input image, S Represent a point p neighborhood, I q express q the gray value of the pixel, I p express p the gray value of the pixel, X p Indicates that the bilateral regression filter at the point p the output value of X q Indicates that the bilateral regression filter at the point q The output value of || q - p || represents a pixel q and pixels p The Euler distance, is a real number, 0≤ ≤1...

Embodiment 2

[0039] The specific implementation process includes the following steps:

[0040] use h ( ) represents the spatial weight of the kernel function (spatial weight), with δ (·) represents the value range kernel function (rangeweight), then the bilateral regression filter (including one-dimensional bilateral regression filter and two-dimensional bilateral regression filter) is shown in formula (2),

[0041] (2)

[0042] In formula (2) I Indicates the gray level of the image, S Represent a point p neighborhood, I q express q the gray value of the pixel, I p express p the gray value of the pixel, X p Indicates that the bilateral regression filter at the point p the output value of X q Indicates that the bilateral regression filter at the point q The output value of || q - p || represents a pixel q and pixels p Euler distance of . is a real number, 0≤ ≤1, can be used to control the performance of the filter, The closer it is to 0, the stronger the edge...

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Abstract

The invention relates to a bilateral regression filter method for grayscale and colored images. As an effective information carrier, images can be used as an important manner for obtaining and exchanging effective information by users; but the images systematically import certain noises due to various interference factors in the forming, transmission and reception processes, some detail features are hidden in the noises, and great difficulties are brought to the image processing processes such as image observation as well as feature information extraction and analysis, so that adopting a proper method to remove pollution noises in the images is a very important pre-processing step. The bilateral regression filter method provided by the invention can be used for the filtering of grayscale and colored images.

Description

Technical field: [0001] The invention relates to a filtering method applicable to grayscale and color images, which is named bilateral regression filtering. Background technique: [0002] As an effective information carrier, images are an important way for people to obtain and communicate effective information. However, in the process of image formation, transmission, and reception, certain noises will be systematically introduced due to the existence of various interference factors, and some detailed features are often submerged in the noise. posed great difficulties. It is a very important preprocessing step to adopt an appropriate method to remove the pollution noise in the image. [0003] Common filtering methods will blur the edge of the image contour while removing noise, such as Butterworth filter and Gaussian filter. The edge of the image contour contains important information of the image, so an important issue in image filtering is to maintain the edge of the im...

Claims

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T5/001G06T2207/20028
Inventor 朴伟英张浩林海军张旭辉
Owner HARBIN UNIV OF SCI & TECH
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