A Parameter Adaptive Method for Image Smoothing

作     者：Suwei Wang Xiang Ma Xuemei Li 

作者机构：School of Computer Science and TechnologyShandong UniversityJinan 250101China School of SoftwareShandong UniversityJinan 250101China 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报自然科学版（英文版）)

年 卷 期：2024年第29卷第4期

页      面：1138-1151页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：image smoothing parameter adaptation bicubic interpolation polynomial fitting 

摘      要：Edge is the key information in the process of image smoothing. Some edges, especially the weak edges, are difficult to maintain, which result in the local area being over-smoothed. For the protection of weak edges, we propose an image smoothing algorithm based on global sparse structure and parameter adaptation. The algorithm decomposes the image into high frequency and low frequency part based on global sparse structure. The low frequency part contains less texture information which is relatively easy to smoothen. The high frequency part is more sensitive to edge information so it is more suitable for the selection of smoothing parameters. To reduce the computational complexity and improve the effect, we propose a bicubic polynomial fitting method to fit all the sample values into a surface. Finally, we use Alternating Direction Method of Multipliers (ADMM) to unify the whole algorithm and obtain the smoothed results by iterative optimization. Compared with traditional methods and deep learning methods, as well as the application tasks of edge extraction, image abstraction, pseudo-boundary removal, and image enhancement, it shows that our algorithm can preserve the local weak edge of the image more effectively, and the visual effect of smoothed results is better.