A piecewise gradient prior for small structures and contrast preserving image smoothing

Tingting Li, Fang Li and Huiqing Qi

Applied Mathematics and Computation, 2025, vol. 507, issue C

Abstract: Image smoothing is a fundamental task in digital image processing with broad applications. However, traditional texture smoothing techniques often result in the loss or blurring of small structural information and contrast. In this paper, we introduce a piecewise gradient prior aimed at overcoming this drawback. The prior is based on a four-segment piecewise (FSP) penalty function, which can process signals at different scales. We also present an effective iterative algorithm based on the alternate direction method of multipliers framework and provide theoretical proof of global convergence for the proposed algorithm. Our method has shown promising results in various applications, including texture removal, clip art compression artifact removal, and edge detection. Experimental results demonstrate the effectiveness and superior performance of our approach in these applications.

Keywords: Image smoothing; Piecewise gradient prior; Alternating direction method of multipliers (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300325002838
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:507:y:2025:i:c:s0096300325002838

DOI: 10.1016/j.amc.2025.129557

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().