With the progress of science and technology and the arrival of the big data era, people increasingly rely on computers to deal with daily life and related affairs. In recent years, machine learning has become more and more popular and has achieved good results in some fields, which also makes machine learning widely used. Among them, visual neural network technology can more intelligently analyze the emotional expression of oil painting, which is one of the current research hotspots, involving machine vision, pattern recognition, image processing, artificial intelligence, and other fields. However, in the art field, oil painting is still very different from other images. At present, there is no deep learning algorithm to identify the application of emotional expression analysis in oil painting theme creation. This paper will start with the neural network algorithm and combine the big data recognition technology to analyze the emotional expression of the oil painting subject in the public environment and establish the emotional expression analysis model of oil painting creation based on big data and neural network. The experiment shows that the graphics synthesized by this model have high resolution and good definition, but the speed is slow in the process of experimental operation. It takes about one hour to complete a round of image optimization.
This paper establishes the visual communication effect model of public service advertisement based on a fractional differential equation. In this paper, a guided filter is used to smooth the image and improve the grayscale phenomenon of the image. Then this paper adopts a method based on wavelet transform to obtain the reference line of the target point source. This achieves the suppression of the background baseline of the target point. Finally, this paper extracts the edge detail feature value in the public service billboard, which realizes the fusion recognition of the missing part in the advertising image. Then this paper transforms it into the problem of separating the object’s foreground from the image’s background. Finally, this paper uses an improved nonlinear diffusion method to suppress the tiny image detail signal. Experiments show that the algorithm has good optical transmission performance and is not limited by the size of public service advertisement images.
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