Genetic fuzzy optimized approximate multiplier design based non-linear anisotropic diffusion image denoising in VLSI

A social network image denoising algorithm based on multifeature fusion is proposed. Based on the multifeature fusion theory, the process of social network image denoising is regarded as the fitting process of neural network, and a simple and efficient convolution neural structure of multifeature fusion is constructed for image denoising. The gray features of social network image are collected, and the gray values are denoising and cleaning. Based on the image features, multiple denoising is carried out to ensure the accuracy of social network image denoising algorithm and improve the accuracy of image processing. Experiments show that the average noise of the image processed by the algorithm designed in this study is reduced by 8.6905 dB, which is much larger than that of other methods, and the signal-to-noise ratio of the output image is high, which is maintained at about 30 dB, which has a high effect in the process of practical application.

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Genetic fuzzy optimized approximate multiplier design based non-linear anisotropic diffusion image denoising in VLSI

Cited by 3 publications

References 25 publications

Energy efficient enhanced all pass transformation fostered variable digital filter design based on approximate adder and approximate multiplier for eradicating sensor nodes noise

Energy efficient enhanced all pass transformation fostered variable digital filter design based on approximate adder and approximate multiplier for eradicating sensor nodes noise

Floorplanning for optimizing area using sequence pair and hybrid optimization

Image denoising algorithm of social network based on multifeature fusion

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