RESEARCH ON AN ADAPTIVE IMAGE ENHANCEMENT ALGORITHM FOR LACQUER PAINTING IMAGES IN LOW-LIGHT ENVIRONMENTS BASED ON THE SPARROW SEARCH ALGORITHM AND INCOMPLETE BETA FUNCTION
DS 136: Proceedings of the Asia Design and Innovation Conference (ADIC) 2024
Year: 2024
Editor: Yong Se Kim; Yutaka Nomaguchi; Chun-Hsien Chen; Xiangyang Xin; Linna Hu; Meng Wang
Author: Wang, Bin; Wang, Jianfeng; Bao, Qian; Liang, Na
Series: Other endorsed
Institution: Hanyang University
Page(s): 102-110
Abstract
Lacquer painting, a significant component of China’s cultural heritage, faces challenges such as insufficient lighting, texture loss, and blurred details due to environmental degradation and time. Traditional restoration methods are often inefficient in addressing these complex issues. To enhance restoration precision and speed, this paper proposes an adaptive image enhancement algorithm that integrates the Sparrow Search Algorithm (SSA) with the incomplete beta function. This novel approach leverages SSA's optimization capability to dynamically determine optimal grayscale transformation parameters, enabling effective image enhancement in low-light and intricate texture conditions. Experimental results demonstrate significant improvements in image clarity and detail restoration, outperforming traditional methods, particularly in terms of visual perception and brightness contrast. The proposed method automates the enhancement and restoration process, reducing manual intervention and improving efficiency. Its adaptability makes it suitable for various cultural heritage restoration tasks, especially in low-light and severely damaged scenarios. Looking forward, this method holds promise not only for other types of heritage restoration but also as a new approach for the digital preservation of cultural artifacts, with broad application potential.
Keywords: Lacquer Painting Preservation, Sparrow Search Algorithm, Digital Image Processing, Incomplete Beta Function