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Image Style Conversion Model Design Based on Generative Adversarial Networks
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DOI：10.1109/ACCESS.2024.3452677.png)
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摘要
En 中文
The purpose of image style conversion is to transfer the style of one image to another, so that the target image retains the original content and the style of the reference image. An image style conversion technique based on generative adversarial network is proposed. This study innovatively adopts dual synthesizer and dual discriminator structure to improve the quality and efficiency of style conversion, and introduces extended convolution to enhance feature extraction. Combined with a well-designed loss function to optimize the style conversion process, a convolutional module reconstruction generator network including linear computation is added. The experimental results showed that in the training time test, the research method maintained a training time of less than 97 seconds when the number of input style types increased to 18. When conducting image style loss testing, the research method found that the image style loss value was lower compared to other techniques when the input size was 1080p and the pixel count was 10M. In the analysis of pixel loss during image style conversion, the research method shows that the pixel loss after processing virtual images is only 3.5k out of 10M, which hardly affects the expression of image content. The designed image style conversion model can accomplish the task of image style conversion with high quality and high efficiency.
Keyword:
Generators
Generative adversarial networks
Training
Feature extraction
Image processing
Image edge detection
Convolution
image processing
linear calculation
convolution
image style
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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