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Dual-domain decomposition mine noise-containing image enhancing method

An underground and image technology, applied in the field of image processing, can solve the problems of low image contrast, weak performance, poor imaging environment, etc.

Active Publication Date: 2019-12-31
CHINA UNIV OF MINING & TECH (BEIJING)
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Problems solved by technology

Compared with the other types of enhancement algorithms mentioned above, the transformation domain image enhancement algorithm has unique advantages in separation and noise suppression. However, the transformation domain image enhancement algorithm has weak performance in improving image contrast and brightness, and the underground imaging environment is poor, and the image contrast is low. Underground image enhancement algorithms must have a strong ability to improve contrast

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  • Dual-domain decomposition mine noise-containing image enhancing method
  • Dual-domain decomposition mine noise-containing image enhancing method
  • Dual-domain decomposition mine noise-containing image enhancing method

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Embodiment Construction

[0037] The present invention will be described in detail below in conjunction with the accompanying drawings and implementation examples.

[0038] A method for enhancing noise-containing images in underground mines by double-domain decomposition, comprising the following steps:

[0039] The first step: input image r, g, b channel layered decomposition

[0040] Such as figure 1 As shown, the Gaussian filter is used to decompose the input image according to three channels of r, g, and b. The Gaussian filter is a spatial smoothing filter. The template size of the Gaussian kernel function of the Gaussian filter is 15×15, and the variance is 3. In the dotted frame of "image r, g, b channel layered decomposition", the base layer r, g, b channel image by the input image f c ,c∈{r,g,b} and Gaussian kernel function It is obtained by convolution, and the r, g, b channel images of the detail layer are obtained by f c ,c∈{r,g,b} minus yields, namely:

[0041]

[0042] Step 2...

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Abstract

The invention discloses a dual-domain decomposition mine noise-containing image enhancing method, which comprises the following steps: firstly, hierarchically decomposing a mine noise-containing imageby using a spatial domain Gaussian filter to realize decoupling of image contrast improvement and noise suppression; secondly, taking the maximum bright channel image, obtained through layering, of the base layer as illuminance component estimation of a Retinex model, and improving the contrast of the base layer according to the Retinex enhancement principle; decomposing a detail layer obtained by layering by using non-subsampled shear wave transformation; carrying out hard threshold shrinkage on the decomposition coefficient in the shear wave transform domain, realizing detail layer noise suppression and non-subsampled shear wave inverse transform shrinkage decomposition coefficients, obtaining a noise suppression detail layer, and realizing enhancement of the noise suppression detail layer by using the same enhancement proportion of the base layer; and finally, fusing the enhanced base layer and the enhanced detail layer for noise suppression to obtain a fused enhanced image, and performing Gamma fine adjustment on the fused enhanced image to obtain a final enhanced image for noise suppression and contrast improvement.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a dual-domain decomposition method for enhancing noise-containing images in underground mines. Background technique [0002] With the acceleration of the development of smart mines, various image recognition technologies continue to extend to underground mines. However, the imaging environment in underground mines is poor and there are many electromagnetic interferences, resulting in low contrast and rich noise in the collected images. Mine image enhancement and noise suppression have become mine images. Identify and apply the top issues that need to be addressed. [0003] At present, commonly used algorithms for image enhancement include: histogram equalization, histogram regulation, image enhancement algorithm based on physical model, image enhancement algorithm based on partial differential equation and variation, and image enhancement algorithm in change ...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06T5/50G06K9/40
CPCG06T5/50G06T2207/20064G06T2207/20221G06V10/30G06T5/90G06T5/70
Inventor 田子建王满利张向阳
Owner CHINA UNIV OF MINING & TECH (BEIJING)
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