Fuzzy line enhancement scheme of computer art image based on DRL algorithm

Article type: Research Article

Authors: Guo, Yong

Affiliations: School of Art and Design, Guangzhou Institute of Technology, Guangzhou, Guangdong 510075, China | E-mail: guoyong202110@126.com

Correspondence: [*] Corresponding author: School of Art and Design, Guangzhou Institute of Technology, Guangzhou, Guangdong 510075, China. E-mail: guoyong202110@126.com.

Abstract: In this study, a new blurred line enhancement scheme of computer art image based on Deep Reinforcement Learning (DRL) algorithm was proposed. The hard threshold method is adopted to remove the noise of computer art images and the blurred lines are extracted by texture separation method. Based on the line extraction results, the Deep Q-Network (DQN) model was built with DRL algorithm, and the sample images were input into the model, and the fuzzy line enhancement results of computer art images were obtained in the output layer. The proposed method exhibits excellent noise reduction effect, and the fuzzy line enhancement quality of computer art image is good. The average enhancement time is 0.58 s, and the practical application effect is good.

Keywords: DRL algorithm, computer art image, fuzzy line enhancement, texture separation, DQN model

DOI: 10.3233/JCM-226450

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 23, no. 2, pp. 949-961, 2023