Abstract
This paper presents an indepth analysis and research on the quantitative design of fine art images through artificial intelligence algorithms. A CycleGAN‐based network model for automatic generation of sketches of fine art images is constructed to extract the edge and contour features of fine art images. The network uses 512 × 1024 high‐resolution art images as input and Pitchman as a discriminator. To further enhance the sketch generation effect, a bilateral filtering algorithm is added to the generator model for noise reduction, and then a K‐means algorithm is used for color quantization to solve the problem of cluttered lines in the generated sketches. The experimental results show that the network model can effectively realize the automatic generation of art image sketches and can retain the detailed part of the costume information well. A rendering platform is built to realize the application of art image generation algorithms and coloring algorithms. The platform integrates the functions of image preprocessing, sketch generation, and sketch coloring, demonstrates the results of the main research content of this paper, and finally increases the interest of the system through the rendering function of the art image grid, which further improves the practicality of the platform.
Highlights
With the development of image processing technology, various types of image processing have become a necessary part of production and life
The image processing process relies on high-quality raw images, and if the acquired images themselves are of low quality, it will affect the effect of the processing and will not be able to play the value of the images themselves
Image enhancement refers to a series of processing according to certain specific requirements for the captured image with poor quality, and the algorithm is used to enhance the information expressed by the image subject and suppress the disturbing information in the image to improve the image quality and provide a better data source for the later image processing [3]
Summary
With the development of image processing technology, various types of image processing have become a necessary part of production and life. From the perspective of different convolutional layers, the shallow convolutional layer has a function like edge detection and can basically extract the texture information of the image. To address these problems, image enhancement technology can play an important role in improving the display of images and making them more accurate in conveying information. Image enhancement refers to a series of processing according to certain specific requirements for the captured image with poor quality, and the algorithm is used to enhance the information expressed by the image subject and suppress the disturbing information in the image to improve the image quality and provide a better data source for the later image processing [3]. Image enhancement plays a key role in the image data preprocessing stage and occupies an irreplaceable position in the digital image processing process
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