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Classifier-guided Multi-style Tile Image Generation Method

瓦片 人工智能 计算机科学 图像拼接 计算机视觉 分类器(UML) 纹理合成 图像纹理 图像(数学) 模式识别(心理学) 图像分割 艺术 视觉艺术
作者
Jianfeng Lu,Mengtao Shi,Changcheng Song,Wangbo Zhao,Lifeng Xi,Mahmoud Emam
出处
期刊:Journal of King Saud University - Computer and Information Sciences [Elsevier BV]
卷期号:36 (1): 101899-101899
标识
DOI:10.1016/j.jksuci.2023.101899
摘要

Image generative models for ceramic tile design lack style diversity and controllability of high-quality generated styles. It is difficult to find a series of ceramic tiles with the same texture but distinct styles, that makes it a challenging for users to select from a limited number of tiles with a single style. Although, Generative Adversarial Networks (GANs) can slightly increase the style diversity of tile images, the style controllability remains very weak. Additionally, concatenating generated tile image blocks to obtain a larger texture region can easily result in seams at the boundaries that decrease image quality. In this paper, we propose a style transfer method for ceramic tiles texture generation that combines a classifier-guided StyleGAN with AdaIN-GAN to overcome the above limitations. Firstly, we introduce a new conditional classifier-guided module into the StyleGAN. With the guidance of the input condition vector, the output image is made to have the tile style characteristics that match the vector. At the same time, the fusion of the condition vectors realizes the style gradient effect of the tile image to expand the style diversity. Secondly, we use the AdaIN-GAN to color the original texture in tile style. The style images generated by StyleGAN are then used as a dataset for model training to enhance the generalization ability of the model and achieve a style transfer effect with fixed texture features but significantly diverse styles. Finally, a linear weighted image stitching method is adopted, which uses an adaptive kernel linear weighted matrix to cover and splice arbitrary seams with image blocks, thereby successfully eliminating seams and enhancing image continuity. When this method is applied to high-resolution tile image generation, the method still maintains higher continuity and clearer image quality. Extensive experiments and human evaluation confirm the superior performance of the proposed method compared with other SOTA methods. The experimental results also verify that the new tile images generated by the proposed algorithm have diverse styles and meet the design requirements for tile style diversity.
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