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An Image Denoising Method Using Total Variation Minimization and Gray Level Co-occurrence Matrix

A gray-scale co-occurrence matrix and minimization technology, applied in the field of image processing, can solve the problems of easy misjudgment, sensitivity, and low accuracy of image edge position, so as to reduce the impact and improve the robustness.

Active Publication Date: 2015-08-12
哈尔滨哈船导航技术有限公司
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Problems solved by technology

However, the model is sensitive to the selection of the threshold value, and the accuracy of using the gradient modulus to judge the position of the edge of the image is not high, and it is easy to misjudgment

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  • An Image Denoising Method Using Total Variation Minimization and Gray Level Co-occurrence Matrix
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  • An Image Denoising Method Using Total Variation Minimization and Gray Level Co-occurrence Matrix

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Embodiment Construction

[0049] The present invention is described in more detail below in conjunction with accompanying drawing example:

[0050] to combine figure 1 ~5, the implementation process of the image denoising method using total variation minimization and gray level co-occurrence matrix of the present invention is as follows figure 1 shown, including the following steps:

[0051] Step 1: Perform Gaussian filtering on the original noisy image.

[0052]Let the noisy image be X, its size is M×N, and the gray scale range is [0,255]. Preprocess the image X with a Gaussian filter to remove some isolated noise points in the non-edge area and reduce the possibility of taking these noises as edges. Among them, the window size of the Gaussian filter is G×G, and the variance is σ. The image obtained after Gaussian filtering is denoted as X'.

[0053] Step 2, use the detection window to traverse the image obtained in step 1, and obtain four gray-level co-occurrence matrices of the sub-image blocks...

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Abstract

The invention aims to provide an image denoising method utilizing total variation minimization and gray scale co-occurrence matrixes. The image denoising method comprises the following steps: Gaussian filtering is conducted on an original noisy image; four gray scale co-occurrence matrixes of a sub-image block in each detection window are gained through images acquired by detection window ergodic; a contrast ratio image is gained through the obtained gray scale co-occurrence matrixes; and noise interference in the original noisy image is removed through a total variation minimization model and each scattering model. The image denoising method utilizing the total variation minimization and the gray scale co-occurrence matrixes improves detection precisions of positions of texture information such as rims, uses the contrast ration image to transit between a total variation minimization denoising method and an isotropism scattering denoising method in a self-adapting mode, has combined advantages of denoising and edge protection, and can effectively reduce effect from staircase effect.

Description

technical field [0001] The invention relates to an image processing method. Background technique [0002] In the process of image imaging and transmission, it is inevitable to be interfered by various noises, which will bring many adverse effects to the subsequent use and analysis of the image. Therefore, image denoising is one of the basic tasks of digital image processing and a prerequisite for image operations such as image feature extraction, segmentation, and pattern recognition. [0003] In recent years, the image denoising method based on total variation minimization proposed by Rudin et al. has been widely researched and applied in the field of image denoising because it can effectively protect image edges while denoising, and has received more and more attention. researchers' attention. Usually, the total variation of noisy images is significantly larger than that of non-noisy images. The basic idea of ​​total variation minimization denoising is to achieve the pur...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00
Inventor 袁赣南韩自发张杰董静赵玉新李涛宋成业李强郭瑞亮
Owner 哈尔滨哈船导航技术有限公司