Resumen de Modeling of fractional differential equation in cloud computing image fusionalgorithm
Xuefeng Yang, Jun Zheng, Chong Xu, Lin Feng, Jamal AlSultan
In order to solve the problems of poor image quality, low definition and loss of image information in traditional algorithms, a modeling research of fractional differential equation in cloud computing image fusion algorithmis proposed. Firstly, the method of image denoising and enhancement under the framework of fractional calculus theory and the improved algorithm based on it are discussed. A series of difficult problems in the image processing method based on fractional calculus are discussed. Then, the large data video image in cloudcomputing environment is fused in scale space through fractional differential equation, and the fused image is decomposed by lifting fractional differential equation to obtain the low-frequency subband coefficients andhigh-frequency subband coefficients in different scale space. For the low-frequency subband coefficients andhigh-frequency subband coefficients, their respective fusion schemes are given to obtain the lifting static small transform coefficients of the fused image. The fusion effect of the proposed algorithm is tested frombothsubjective and objective aspects.The results show that the entropy value of this algorithm is 7.1450, slightlyhigher than sparse coding algorithm and random walk algorithm, which shows that the fusion processing by this algorithm will not lose the amount of information contained in the image. Therefore, the image denoising andenhancement algorithm of fractional differential equation proposed in this paper has good subjective fusioneffect, good clarity and quality of the fused image, and will not lose the information contained in the image
