Image processing is closely related to many disciplines, such as pattern recognition, artificial intelligence, expert system, computer graphics and so on. Image processing is the premise of image understanding and recognition. The effect of image processing model and algorithm has a great impact on subsequent image recognition and understanding . At present, image processing mainly includes image denoising, image amplification, image restoration, image compression, image segmentation, image super-resolution reconstruction and so on. The image to be processed often contains many different components, which mainly include image cartoon, edge, texture and noise. In the process of image processing, it is often necessary to distinguish and process these different image components. For example, image denoising is actually to distinguish image information and noise; The most important thing of image compression is to distinguish and process the important structures (edges and textures) of the image; The super-resolution reconstruction of image actually needs to more clearly represent the important structures such as edge and texture of image . In order to better distinguish and process the different components in the image, it is necessary to describe these different components, that is, appropriate mathematical modeling of these different components.
In the cloud computing environment, large data video images contain a lot of information. There are usually multiple images for the same scene, and the information description is insufficient. Therefore, it is necessary to fuse multiple video image information together to obtain a more accurate and reliable description of the same scene. Instead of simply superimposing video images together, it forms a composite image containing more information to reduce the uncertainty of information . Image fusion based on Fractional Partial Differential Equations (fpde) is a new interdisciplinary branch. Compared with the image denoising method based on integer partial differential equations, the fractional partial differential equation method for image denoising has the advantages of high computational efficiency, good accuracy, strong noise suppression ability and strong edge feature retention ability, The research on numerical method of fractional image denoising model has important application value . H Li and others proposed an image denoising model based on spatial fractional partial differential equation, which can solve the shortcomings that the traditional low order integer partial differential equation denoising model is prone to ladder effect and the high order integer partial differential equation denoising model has poor denoising effect, The fractional order denoising model is constructed by using the fractional derivative as a measure to describe the image, and then the eider Lagrange equation of the corresponding energy functional is obtained according to the relevant theories and properties of the variational method . Zhang C. and others proposed a global variational image denoising method based on the exchange movement space, which not only fully utilizes the prior knowledge of the image, but also adaptively sets the regularization parameters according to the local features of the noisy image, but This method has the disadvantages of complex structure, large memory usage and long time consumption .
Based on this research, a modeling research of fractional differential equation in cloud computing image fusion algorithm is proposed. Experimental results show that the proposed algorithm has good fusion effect.
If for function
The grumwald letnikov definition of fractional differential equations (hereinafter referred to as G-L definition) originates from the operation rules of classical integer order derivatives of continuous functions, and the fractional order is derived from the deduced integer order. The G-L definition of fractional differential equation is very suitable for signal processing. Because it can be converted into convolution operation, it is more practical.
The function f (T) is continuously differentiable in interval
Scale space theory is to use the scale transformation of the original video image in the cloud computing environment to obtain the scale space description sequence of the video image under multi-scale, complete the collection of the main contour of the above sequence scale space, and regard the collected main contour as the feature vector to extract the features on the edge of the video image and various resolutions . The scale space belongs to the description of the region, not the description of the edge, and is defined as follows:
In Gaussian scale space, the same kind of feature points and edges have causality on different scales, that is, when the scale changes, new feature points will appear to a large extent, while existing feature points will shift or disappear to a large extent. The ambiguity caused by the above causality can not be avoided, but can be reduced. However, because the Gaussian kernel has the characteristics of translation and rotation invariance, the first derivative obtained by the Gaussian kernel will transform the Harris corner operator into the description of scale space. Gaussian kernel is described as follows:
Large data video images in cloud computing environment are fused in scale space through the lifting static wavelet transform algorithm with translation invariance, because the algorithm not only has better local characteristics in spatial and frequency domain, but also has translation invariance, which can avoid the image distortion caused by the traditional lifting wavelet transform. Because the scale space can effectively use the inter layer correlation of wavelet coefficients to enhance the image edge and reduce the impact of noise, this section adds the scale space to the lifting static wavelet transform domain to realize big data video image fusion. The basic idea of scale space fusion of big data video images in cloud computing environment is as follows: firstly, the fused image is decomposed by lifting wavelet transform to obtain low-frequency subband coefficients and high-frequency subband coefficients in different scale spaces; Then, the fusion schemes for low-frequency subband coefficients and high-frequency subband coefficients are given, and the lifting static wavelet transform coefficients of the fused image are obtained; The final fused image is obtained by lifting the inverse static wavelet transform .
The decomposition and reconstruction diagram of lifting static wavelet transform is described in Figure 1.
In Figure. 1,
In the cloud computing environment, the low-frequency part of big data video image obtained after lifting static wavelet transform decomposition is the embodiment of the main energy of the image, which directly affects the contour of the video image. Therefore, selecting a reasonable low-frequency subband coefficient plays a very key role in enhancing the visual effect of the fused image. A large number of studies show that people's vision is very sensitive to the change of image local contrast, so the concept of local visual visibility which can reflect the contrast of video image is introduced. The formula is described as follows:
A and B are used to describe two source images in big data video images in cloud computing environment. The detailed fusion process for low-frequency subband coefficients is as follows:
The high-frequency subband of the video image obtained after the lifting static wavelet transform is the embodiment of the image edge information. Because the Laplace operator can reflect the image gradient information, the Laplace operator is introduced based on the lifting static wavelet transform, and its formula is described as follows:
Because the vision system is relatively sensitive to the change of local contrast of video image, the concept of visual characteristic contrast is given, and its formula is described as follows:
In order to further verify the effectiveness of this algorithm, the fusion results need to be evaluated by objective evaluation indexes. Image sharpness is the key index to evaluate the fusion algorithm. For the image with size (m, n), the gray level at f (I, J) is described by F (I, J). The image sharpness can be described as:
By analyzing Figure. 2, it can be seen that when x gradually increases, the image definition becomes higher and higher, indicating that the fusion effect of the algorithm in this paper is very good. When x increases to a certain extent, G (x) tends to be stable, indicating that x is not the bigger the better. By analyzing Figure. 3, it can be seen that when x gradually increases, the amount of calculation also gradually increases, but on the whole, the amount of calculation is less, which shows that the algorithm in this paper can not only ensure the clarity of the fused image, but also has less amount of calculation.
In order to comprehensively evaluate the fusion effect of this algorithm, entropy, standard mean square deviation, signal-to-noise ratio and quality measurement index are tested as evaluation indexes.
The calculation formula of entropy is as follows:
The standard mean square deviation is the embodiment of the retention degree of the fused image to the source image information. The greater the standard mean square difference, the lower the retention degree of the fused image to the source image information. The signal-to-noise ratio is opposite to the standard mean square deviation. The higher its value, the better the fusion effect of the algorithm .
The quality measurement index Q (a, B, f) measures the quality of the fused image by using the local quality, and its formula is described as follows:
The sparse coding algorithm and random walk algorithm are tested by comparison. The three algorithms are used to fuse the big data video images in the cloud computing environment. The entropy, standard mean square deviation, signal-to-noise ratio and quality measurement index of the fused images are compared. The results are described in Table 1.
Objective evaluation of image fusion results of three algorithms
|Algorithm||Entropy||Standard mean square deviation||Signal to noise ratio||Quality measurement index|
|Sparse coding algorithm||7.0021||505.81||12.058||0.711|
|Random walk algorithm||7.0882||762.23||11.261||0.676|
In this paper, the modeling of fractional differential equation in cloud computing image fusion algorithm is deeply studied, and a new scale space fusion algorithm of big data video image in cloud computing environment is proposed. The scale space theory is introduced, and the large data video images in cloud computing environment are fused in scale space through the lifting static wavelet transform algorithm with translation invariance. Experimental results show that the proposed algorithm has good fusion effect.
Objective evaluation of image fusion results of three algorithms
|Algorithm||Entropy||Standard mean square deviation||Signal to noise ratio||Quality measurement index|
|Sparse coding algorithm||7.0021||505.81||12.058||0.711|
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