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Medical image noise reduction method based on structure clustering and sparse dictionary learning

A sparse dictionary and structural clustering technology, applied in the field of image processing, can solve the problems of not being able to effectively maintain the image edge, the denoising effect of the uniform area of ​​the image is not ideal, ignoring the similarity of the image block structure, etc., to achieve good visual effects, The effect of removing noise from an image

Active Publication Date: 2014-06-11
XIDIAN UNIV
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Problems solved by technology

Although this method can enhance the adaptability of the dictionary, it still has the disadvantage that the dictionary learning method ignores the structural similarity of the image block, cannot effectively maintain the edge of the image, and the denoising effect on the uniform area of ​​​​the image is not ideal.

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  • Medical image noise reduction method based on structure clustering and sparse dictionary learning
  • Medical image noise reduction method based on structure clustering and sparse dictionary learning
  • Medical image noise reduction method based on structure clustering and sparse dictionary learning

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Embodiment Construction

[0038] The present invention will be further described below in conjunction with the accompanying drawings.

[0039] Refer to attached figure 1 , the concrete steps of the present invention are as follows:

[0040] Step 1: Input a noisy image.

[0041] Input an optional noise image containing additive white Gaussian noise.

[0042] Step 2: Prefiltering.

[0043] The generalized K-means K-SVD method is used to filter the noisy image to obtain the filtered image.

[0044] The concrete steps of described generalized K mean value K-SVD method are as follows:

[0045] In the first step, the size of the image block is set to 8×8, the size of the over-complete dictionary is 64×256, and the over-complete dictionary is initialized as a discrete cosine transform DCT dictionary.

[0046] In the second step, calculate the sparse coding coefficient according to the following formula:

[0047] α mn = arg min ...

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Abstract

The invention discloses a medical image noise reduction method based on structure clustering and sparse dictionary learning, which overcomes the problems that in the prior art, the dictionary learning method neglects the structure similarity of image blocks, so that detailed information of images is lost, and the homogeneous region of images is not smooth. The medical image noise reduction method based on structure clustering and sparse dictionary learning, which is provided by the invention, comprises the following implementation steps: (1) inputting noise images; (2) pre-filtering; (3) clustering structures; (4) extracting various training sample sets; (5) learning sparse dictionaries in classification; (6) acquiring the final training dictionary; (7) acquiring various noise reduction estimation values; (8) acquiring images after noise suppression; (9) outputting images after noise suppression. The medical image noise reduction method based on structure clustering and sparse dictionary learning, which is provided by the invention, has the advantages of effectively removed noise, improved vision effect of images, effectively kept edges and patterns, and homogenized regional smoothness, and has the capability of being applied to medical computed tomography and noise reduction treatment of nuclear magnetic resonance images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a medical image noise reduction method based on structural clustering and sparse dictionary learning in the technical field of image noise reduction containing additive noise. The invention can be used for denoising processing of medical computed tomography (CT) and nuclear magnetic resonance (magnetic resonance MR) images. Background technique [0002] The purpose of image denoising is to protect the feature information of the image, such as the edge and texture of the image, while removing the noise. In the process of image acquisition, processing and transmission, due to technical limitations and the inherent characteristics of the equipment itself, the image inevitably contains various noises, resulting in a serious decline in image quality and greatly affecting subsequent image processing. Therefore, image noise reduction is of great significance. [0003] W...

Claims

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Application Information

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IPC IPC(8): G06T5/00
Inventor 白静王爽范婷焦李成韩雪云张向荣马文萍马晶晶
Owner XIDIAN UNIV
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