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A multi-focus image fusion method

A multi-focus image and image fusion technology, which is applied in the field of multi-focus image fusion, can solve the problems of image block size fusion image block effect and unsatisfactory fusion effect, etc., to suppress the block effect, improve the quality of the fusion image, and achieve high recognition. The effect of accuracy

Inactive Publication Date: 2016-04-20
NORTHWEST UNIV
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0011] The technical problem to be solved by the present invention is that in the field of multi-focus image fusion, due to the inability to adaptively determine the image block size, block effects appear in the fused image, and the fusion effect is not ideal.

Method used

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Embodiment 1

[0054] Following the technical scheme of the present invention, this embodiment is figure 2 The two source images shown in (a) and (b) are fused, and the processing results are as follows image 3 shown. At the same time, five image fusion methods, wavelet transform (DWT), non-subsampling-based contourlet transform (NSCT), principal component analysis (PCA), spatial frequency (SF), and pulse-coupled neural network (PCNN), are used to process images. figure 2 The two source images shown in (a) and (b) are fused, and the result is as follows image 3 As shown, the quality evaluation of the fused images of different fusion methods is carried out, and the results shown in Table 1 are obtained through processing and calculation.

[0055] Table 1 Multi-focus image 'rose' fusion image quality evaluation.

[0056]

Embodiment 2

[0058] Following the technical scheme of the present invention, this embodiment is figure 2 The two source images shown in (c) and (d) are fused, and the processing results are as follows Figure 5 shown.

[0059] At the same time, five image fusion methods, wavelet transform (DWT), non-subsampling-based contourlet transform (NSCT), principal component analysis (PCA), spatial frequency (SF), and pulse-coupled neural network (PCNN), are used to process images. figure 2 The two source images (c) and (d) shown in the fusion process, the result is as follows Figure 5 shown, yes Figure 5 The quality of the fused images of different fusion methods is evaluated, and the results shown in Table 2 are processed and calculated.

[0060] Table 2 Multi-focus image 'lab' fusion image quality evaluation.

[0061]

[0062] In Table 1 and Table 2: Method represents the method; the fusion method includes five types: wavelet transform (DWT), non-subsampling-based contourlet transform ...

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Abstract

The invention discloses a multi-focus image fusion method. This method first decomposes the multi-focus image by robust principal component analysis to obtain the sparse component matrix corresponding to the source image; secondly, the sparse component matrix is ​​weighted and averaged to obtain a temporary sparse component matrix and a quadtree is performed on the temporary sparse component matrix Decomposition; calculate the gradient energy of the corresponding matrix sub-blocks of the sparse component matrix of the source image according to the QT decomposition results, and construct the fusion decision matrix by comparing the gradient energy; finally, merge the corresponding image sub-blocks of the source image according to the decision matrix to obtain the fusion image. By combining RPCA and QT decomposition, this method solves the problem that the image block size cannot be divided adaptively, which can effectively suppress the block effect and improve the quality of image fusion.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a multi-focus image fusion method. Background technique [0002] Multi-focus image fusion is to use a certain fusion algorithm to extract the clear areas of multiple focused images in a scene obtained under the same imaging conditions after registration, and combine these areas to generate a single image of all the images in the scene. Clear images of objects. It is widely used in transportation, medical care, security, logistics and other fields. It can effectively improve the utilization rate of the sensor image information and the reliability of the system to detect and recognize the target table. [0003] Pixel-level image fusion directly adopts an appropriate fusion algorithm in the original image pixel gray space for fusion processing, the main purpose is to provide support for subsequent image enhancement, image segmentation and image classification ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/50
Inventor 陈莉张永新唐晓芬牛发发李亮尚军王珊珊周琳吕英杰刘健李青
Owner NORTHWEST UNIV
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