Multi-focus image fusion method

A multi-focus image and image fusion technology, applied in the field of multi-focus image fusion, can solve the problems of unsatisfactory fusion effect and image block effect of image block size fusion image, so as to suppress the block effect, improve the quality of the fusion image, and accurately extract Effect

Inactive Publication Date: 2013-12-18
NORTHWEST UNIV(CN)
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  • Claims
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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, including wavelet transform (DWT), non-subsampling-based contourlet transform (NSCT), principal component analysis (PCA), spatial frequency (SF), and pulse-coupled neural network (PCNN), are used for image fusion. 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 t...

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Abstract

The invention discloses a multi-focus image fusion method. The method comprises the steps of firstly carrying out robust principal component analysis on a multi-focus image to obtain a spare component matrix corresponding to a source image, then weighting and averaging the sparse component matrix to obtain a temporary spare component matrix and carrying out quadtree decompression on the temporary sparse component matrix, calculating the gradient energy of matrix subblocks corresponding to the sparse component matrix of the source image according to QT decompression results and comparing the gradient energy to construct a fusion decision matrix, and finally combining the image subblocks corresponding to the source image according to the decision matrix to obtain a fusion image. The method solves the problem that image blocks cannot be divided in a self-adaptive mode by combining RPCA with QT decompression, can effectively restrain block effects and improves the image fusion quality.

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 Applications(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(CN)
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