A Method of Image Fusion and Super-resolution Based on Variation and Fractional Differential
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A fractional differential and super-resolution technology, applied in the field of image processing and information fusion, can solve the problems of high spatial resolution and single function of the image to be fused
Active Publication Date: 2016-09-07
云南联合视觉科技有限公司
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[0005] The present invention provides an image fusion and super-resolution implementation method based on variation and fractional differentiation, which is used to solve the problem that the traditional image fusion method has a single function and has high requirements for the spatial resolution of the image to be fused
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Embodiment 1
[0042] Embodiment 1: as Figure 1-19 As shown, an image fusion and super-resolution implementation method based on variation and fractional differentiation, the steps of the method are as follows:
[0043] A. L low-resolution images to be fused from multiple sources as a multi-channel image , while introducing weighted multi-channel images ;in, is the i-th image f i (x, y) weight coefficient;
[0044] B. Use the eigenvalues and eigenvectors of the structural tensor of the weighted multi-channel image f(x, y) in step A to describe the change of its own information, and thus obtain the weighted multi-channel image f(x ,y) gradient information V(x,y);
[0045] C. In the implementation of super-resolution, assuming that the ideal super-resolution fusion image I is known, the low-resolution fusion image HI is obtained by acting on the image through the downsampling operator matrix H;
[0046] D. According to the gradient information V(x, y) obtained in step B and the gr...
Embodiment 2
[0071] Embodiment 2: as Figure 1-19 As shown, an image fusion and super-resolution implementation method based on variation and fractional differentiation, the steps of the method are as follows:
[0072] A. L low-resolution images to be fused from multiple sources as a multi-channel image , while introducing weighted multi-channel images ;in, is the i-th image weight coefficient;
[0073] B. Use the eigenvalues and eigenvectors of the structural tensor of the weighted multi-channel image f(x, y) in step A to describe the change of its own information, and thus obtain the weighted multi-channel image f(x ,y) gradient information V(x,y);
[0074] C. In the implementation of super-resolution, assuming that the ideal super-resolution fusion image I is known, the low-resolution fusion image HI is obtained by applying the downsampling operator matrix H to the image; this process can be expressed as HI= P.I.Q:
[0075] in,
[0076] ,
[0077] ,
[0078] r and ...
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Abstract
The invention relates to an image fusion and super-resolution achievement method based on variation and fractional order differential, and belongs to the field of image processing and information fusion. On the basis of image fusion and super-resolution achievement, a low-resolution source image to be fused is regarded as a multi-channel image, unit value representation of gradient characteristics of the multi-channel image is obtained through construction of structure tensor of the low-resolution source image, and an image fusion and super-resolution achievement model is established according to the same or similar gradient characteristics between the low-resolution fusion image and the multi-channel image; in the model, the fractional order differential and fractional order total-variation minimization achievement method is introduced to achieve noise suppression, image edge information is enhanced through diffusion of two-way filtering wave diffusion, and generation of false information is suppressed. The method overcomes the defect that with a traditional method, fusion and super-resolution achievement can not be performed at the same time and has good application prospects in the fields of target imaging, safety monitoring and the like.
Description
technical field [0001] The invention relates to an image fusion and super-resolution realization method based on variation and fractional differentiation, which belongs to the field of image processing and information fusion. Background technique [0002] Multi-source image fusion is to integrate multiple image information about a specific scene acquired by different types of sensors (or the same type of sensor at different times or in different ways) to generate a new interpretation of the scene, so that A clearer, more complete, more reliable description of the scene or goal. The images obtained through synthesis can effectively overcome the differences and limitations of single sensor image data in terms of geometry, spectrum, time, and spatial resolution, which is very beneficial to the identification, understanding, and positioning of events or physical phenomena. At present, this technology is widely used in computer vision, medical imaging and diagnosis, remote sensi...
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