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Biological image denoising algorithm based on statistical rule

An image and biological technology, which is applied in the field of optical microscopy and metrology microscopic imaging, can solve the problems of not meeting engineering needs and not being able to completely remove noise, and achieve the effect of improving the removal range and removal accuracy

Active Publication Date: 2017-02-15
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] The purpose of the present invention is to solve the problem that the traditional algorithm processing results cannot completely remove the noise, which does not meet the engineering needs, and there are many limitations when the traditional algorithm processes images, and provides a biological image denoising algorithm based on statistical laws

Method used

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  • Biological image denoising algorithm based on statistical rule
  • Biological image denoising algorithm based on statistical rule
  • Biological image denoising algorithm based on statistical rule

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specific Embodiment approach 1

[0020] Specific Embodiment 1: The biological image denoising algorithm based on statistical laws described in this embodiment includes three steps of selecting a filter template, monitoring cell edges and noise, and image denoising processing. The specific process is:

[0021] Step 1. Select a filter template: use a biological microscope to collect RGB images of biological cells, and select a filter template that can separate cells from isolated point noise by traversing the RGB images collected by the biological microscope. The selected filter templates are mean filter templates and Gaussian Filtering template, the RGB image is composed of a single cell and background area stray noise, or a cell group composed of multiple cells and background area stray noise;

[0022] Step 2, cell edge and noise monitoring: use the average filter template and Gaussian filter template to traverse the entire collected biological cell image, and determine the edge position of the biological cell...

specific Embodiment approach 2

[0026] Specific embodiment 2: This embodiment will further explain Embodiment 1. The specific process of using the mean filter template and the Gaussian filter template to traverse the entire biological cell image collected in step 2 to determine the position of the edge of the biological cell and the position of the isolated noise point is as follows: :

[0027] Use the mean filter template and the Gaussian filter template to traverse the entire collected biological cell image, use the Gaussian filter template in each region to obtain the filter threshold of the region, and then use the mean filter template to delete the larger than or If the number is smaller than the filtering threshold, it is determined whether the traversed area is an isolated point noise position or a cell edge or internal position according to the number.

specific Embodiment approach 3

[0028] Specific implementation mode three: this implementation mode further explains implementation mode two, assuming that the brightness value threshold S=48 obtained by the Gaussian filter template, when the brightness value inside the area traversed by the mean value filter template is greater than S=48, the number exceeds the mean value filter 1 / 2 of the template, the area traversed at this time is located at the edge or inside of the cell, and when the brightness value inside the area traversed by the mean filtering template is greater than S=48 and does not exceed 1 / 2 of the mean filtering template, this When traversing the region is located in the outlier noise position.

[0029] In this embodiment, the position of filtering or retaining the original image is selected according to the determined area division of the image, so as to achieve the effect of precise filtering. The image effect after filtering according to this method is as follows: Figure 4 shown.

[0030...

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Abstract

A biological image denoising algorithm based on a statistical rule belongs to the optical microscopic technology and metering microscopic imaging technology fields. By using a traditional algorithm, in a processing result, a noise can not be completely removed so that an engineering need can not be satisfied, and many limitations exists during image processing. The algorithm comprises three steps of filtering template selection, cell edge and noise monitoring, and image denoising processing. A specific process comprises the following steps of using a biological microscope to collect RGB images of biological cells and selecting filtering templates to be a mean value filtering template and a Gaussian filtering template; using the mean value filtering template and the Gaussian filtering template to traverse a collected whole biological cell image and determining a biological cell edge position and an isolated noise point position; and carrying out filtering processing on the biological cell edge position and the isolated noise point position, filtering a isolated point noise, retaining a biological cell sample graph and acquiring a denoised biological cell image. The algorithm is used for biological image denoising.

Description

technical field [0001] The invention relates to a biological image denoising algorithm, which belongs to the technical field of optical microscopic technology and measurement microscopic imaging. Background technique [0002] On the basis of the three-dimensional height information of the sample measured by the confocal microscope, one-dimensional color information is added to form a four-dimensional information map that can directly reflect the material and shape of the sample. The three-dimensional height information can be determined by the tomographic data measured by the confocal microscope. The processing results are relatively accurate, and the color information of each position of the sample is obtained by taking a photo of a color CCD, but the brightness and contrast of the picture obtained by taking a color CCD are not high, and contain many isolated noise points, resulting in the possible distortion of the color picture. The recognition is not good. When we use th...

Claims

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T2207/20024G06T5/70
Inventor 刘俭谭久彬李勇牛斌
Owner HARBIN INST OF TECH
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