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Open Access

A Parameter Adaptive Method for Image Smoothing

School of Computer Science and Technology, Shandong University, Jinan 250101, China
School of Software, Shandong University, Jinan 250101, China
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Abstract

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.

Keywords

image smoothingparameter adaptationbicubic interpolationpolynomial fitting

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Tsinghua Science and Technology
Volume 29 Issue 4,
August 2024
Pages 1138-1151
DOI: 10.26599/TST.2023.9010068
Cite this article:
Wang S, Ma X, Li X. A Parameter Adaptive Method for Image Smoothing. Tsinghua Science and Technology, 2024, 29(4): 1138-1151. https://doi.org/10.26599/TST.2023.9010068

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Received: 29 May 2023
Revised: 26 June 2023
Accepted: 02 July 2023
Published: 09 February 2024
© The Author(s) 2024.

The articles published in this open access journal are distributed under the terms of theCreative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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