Open Access

ARTICLE

A Sketch-Based Generation Model for Diverse Ceramic Tile Images Using Generative Adversarial Network

by Jianfeng Lu^1,*, Xinyi Liu¹, Mengtao Shi¹, Chen Cui^1,2, Mahmoud Emam^1,3,*

1 School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China
2 Key Laboratory of Public Security Information Application Based on Big-Data Architecture, Ministry of Public Security, Zhejiang Police College, Hangzhou, 310000, China
3 Faculty of Artificial Intelligence, Menoufia University, Shebin El-Koom, 32511, Egypt

* Corresponding Author: Jianfeng Lu. Email:

Intelligent Automation & Soft Computing 2023, 37(3), 2865-2882. https://doi.org/10.32604/iasc.2023.039742

Received 14 February 2023; Accepted 20 April 2023; Issue published 11 September 2023

Abstract

Ceramic tiles are one of the most indispensable materials for interior decoration. The ceramic patterns can’t match the design requirements in terms of diversity and interactivity due to their natural textures. In this paper, we propose a sketch-based generation method for generating diverse ceramic tile images based on a hand-drawn sketches using Generative Adversarial Network (GAN). The generated tile images can be tailored to meet the specific needs of the user for the tile textures. The proposed method consists of four steps. Firstly, a dataset of ceramic tile images with diverse distributions is created and then pre-trained based on GAN. Secondly, for each ceramic tile image in the dataset, the corresponding sketch image is generated and then the mapping relationship between the images is trained based on a sketch extraction network using ResNet Block and jump connection to improve the quality of the generated sketches. Thirdly, the sketch style is redefined according to the characteristics of the ceramic tile images and then double cross-domain adversarial loss functions are employed to guide the ceramic tile generation network for fitting in the direction of the sketch style and to improve the training speed. Finally, we apply hidden space perturbation and interpolation for further enriching the output textures style and satisfying the concept of “one style with multiple faces”. We conduct the training process of the proposed generation network on 2583 ceramic tile images dataset. To measure the generative diversity and quality, we use Frechet Inception Distance (FID) and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) metrics. The experimental results prove that the proposed model greatly enhances the generation results of the ceramic tile images, with FID of 32.47 and BRISQUE of 28.44.

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