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Discriminative dictionary learning based multi-source image fusion denoising method

A dictionary learning and source image technology, applied in the field of multi-source image fusion and denoising, can solve the problems of multi-source image denoising and fusion difficulties

Active Publication Date: 2018-06-22
KUNMING UNIV OF SCI & TECH
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Problems solved by technology

[0004] The purpose of the present invention is to provide a method for fusion and denoising of multi-source images based on discriminant dictionary learning, so as to solve the problem of difficulty in denoising and fusion of multi-source images in the prior art

Method used

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  • Discriminative dictionary learning based multi-source image fusion denoising method

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Embodiment 1

[0077] Embodiment 1: as figure 1As shown, the present invention proposes a method for fusion and denoising of multi-source images based on discriminative dictionary learning. Firstly, multi-source images are collected as training samples, and the initial cartoon dictionary and initial texture dictionary are first learned from the training samples through the K-SVD algorithm. In order to improve the discriminative and expressive ability of the dictionary, a new dictionary learning model is proposed by introducing weighted kernel norm constraints. According to the proposed dictionary learning method, the initial dictionary and training samples are used to learn the cartoon dictionary and the texture dictionary; then use MCA The algorithm decomposes the multi-source image to be fused to obtain cartoon components and texture components. At this time, the different components obtained by decomposition are relatively incomplete. By introducing weighted Schatten sparse kernel norm con...

Embodiment 2

[0137] Embodiment 2: adopt traditional ASR, KIM, NSCT, NSCT-SR and Zhu-KSVD method to carry out fusion denoising to the image after adding the noise of embodiment 1, and use Q MI , Q G和 Q P The denoising result is evaluated, and compared with the method of the present invention, Table 1 is the denoising index comparison table of the traditional method and the method of the present invention,

[0138] Table 1 Traditional method and the comparison table of the denoising index of the method of the present invention

[0139]

[0140] The evaluation of image fusion denoising effect includes comprehensive evaluation of subjective visual effect and objective parameter indicators. The subjective effect is observed by human eyes, and the image fusion results are evaluated by three experts in image processing disciplines; the objective evaluation uses mutual information Q MI , Gradient-based evaluation index Q G and the phase-coherence-based index Q P These three parameters are ...

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Abstract

The invention relates to a discriminative dictionary learning based multi-source image fusion denoising method. The method includes: acquiring multi-source images as training samples, and learning thesamples through a K-SVD algorithm to obtain an initial cartoon dictionary and an initial texture dictionary, introducing weighting nuclear norm constraint to bring forward a new dictionary learning model, performing new dictionary learning model learning to obtain a cartoon dictionary and a texture dictionary, decomposing to-be-fused images through an MCA algorithm to obtain a cartoon component and a texture component, introducing weighting Schatten sparse nuclear norm constraint to the cartoon component, adding grey level histogram gradient protection to the texture component to bring forward a new image decomposition model, iterating the model to obtain a cartoon sparse coding coefficient and a texture sparse coding coefficient, respectively fusing to obtain a cartoon component and a texture component according to a principle of maximum sparse coding coefficient l1 norm values of corresponding components, and adding the cartoon component and the texture component to obtain a final fusion image. The method has advantages that image fusion and denoising are realized, false information transferring is avoided, time consumption is reduced, and fusion and denoising performances are improved.

Description

technical field [0001] The invention relates to a multi-source image fusion denoising method based on discriminant dictionary learning, which belongs to the technical field of digital image processing. Background technique [0002] Image fusion refers to the process of image processing and computer technology processing on the image data of the same target collected by multi-source channels, to maximize the extraction of beneficial information in each channel and to remove redundant information, and finally to generate high-quality images comprehensively. , to improve the utilization of image information, improve the accuracy and reliability of computer interpretation, and enhance the spectral resolution and spectral utilization of source images. It has been applied to all aspects of daily life, ranging from medical imaging in medical treatment, community security monitoring and other applications, to national aerospace, military and national defense and other fields. [00...

Claims

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T2207/20081G06T2207/20221G06T5/70
Inventor 李华锋王一棠
Owner KUNMING UNIV OF SCI & TECH
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