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Image fusion method based on non-local sparse K-SVD algorithm

A K-SVD algorithm, a non-local sparse technology, applied in image enhancement, image data processing, computing and other directions, can solve the problem of not fully utilizing the non-local self-similarity of images, achieve good image fusion effect, and improve image fusion effect , the effect of improving performance

Inactive Publication Date: 2014-12-10
高邮欣逸电子商务有限公司
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Problems solved by technology

However, neither the traditional parsing-based dictionaries nor learning-based dictionaries take full advantage of the non-local self-similarity of images

Method used

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  • Image fusion method based on non-local sparse K-SVD algorithm
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  • Image fusion method based on non-local sparse K-SVD algorithm

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Embodiment Construction

[0025] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0026] The processing flow of the image fusion method based on the non-local sparse K-SVD algorithm (refer to the attached figure 1 , 2 ):

[0027] 1) Randomly select m For each selected block, select the r most similar blocks with a p×q size window as the limit, straighten each block and its similar blocks into a column vector, and then connect the first bit to form a new Vector, and finally get a matrix of (r+1) n×m size;

[0028] 2) Use the sparse K-SVD algorithm for dictionary learning to obtain a sparse K-SVD dictionary;

[0029] 3) For an image I k To divide: according to The size of the block divides the image pixel by pixel in the order of raster scanning from the upper left corner to the lower right corner, and straightens the block after division to obtain a matrix Wherein, k represents the label of the image to be fused, i represents the labe...

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Abstract

The invention relates to an image fusion method based on a non-local sparse K-SVD algorithm. The sparse K-SVD algorithm is the dictionary generation algorithm which is presented by Ron Rubinstein and used for image de-noising. A training sample generation process based on image non-local self-similarity is capable of effectively improving the dictionary performance. The image fusion method based on the non-local sparse K-SVD algorithm uses the dictionary which is generated based on the non-local sparse K-SVD algorithm for the image fusion method based on SOMP algorithm so as to generate a better fusion effect. The image fusion method based on the non-local sparse K-SVD algorithm has beneficial effects that the image is fused on a pixel level according to the signal sparse decomposition idea, the dictionary generated based on the sparse K-SVD algorithm effectively combines the analytic dictionary structure and learning dictionary adaptability to improve the signal presentation skill of the dictionary, and meanwhile, the sample selection based on the non-local method improves the dictionary performance, and the image fusion effect is improved.

Description

technical field [0001] The invention belongs to the field of computer image processing and relates to an image fusion method based on a non-local sparse K-SVD algorithm. Background technique [0002] Image fusion is the fusion of images obtained by different sensors or in different ways for the same target or scene into one image, which can reflect the information in multiple original images and describe the scene better than any single image. The source images are all more accurate and comprehensive. Image fusion mainly improves image reliability by processing redundant data among multiple images, and improves image clarity by processing complementary information among multiple images. [0003] The image fusion process can take place at different levels of information description. Image fusion is usually divided into pixel-level fusion, feature-level fusion and decision-level fusion. The pixel-level image fusion method is the most important and basic image fusion method,...

Claims

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Application Information

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IPC IPC(8): G06T5/50
Inventor 李映李方轶张培
Owner 高邮欣逸电子商务有限公司
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