MULTIPARENT FRACTAL IMAGE CODING-BASED METHODS FOR SALT-AND-PEPPER NOISE REMOVAL

Weijie Liang, Xiaoyi Li, Zhihui Tu and Jian Lu
Additional contact information
Weijie Liang: Shenzhen Key Laboratory of Advanced Machine Learning and Applications, School of Mathematical Sciences, Shenzhen University, Shenzhen, 518060, P. R. China2National Center for Applied Mathematics Shenzhen (NCAMS), Shenzhen 518055, P. R. China3The Pazhou Lab, Guangzhou 510335, P. R. China
Xiaoyi Li: Shenzhen Key Laboratory of Advanced Machine Learning and Applications, School of Mathematical Sciences, Shenzhen University, Shenzhen, 518060, P. R. China2National Center for Applied Mathematics Shenzhen (NCAMS), Shenzhen 518055, P. R. China3The Pazhou Lab, Guangzhou 510335, P. R. China
Zhihui Tu: Shenzhen Key Laboratory of Advanced Machine Learning and Applications, School of Mathematical Sciences, Shenzhen University, Shenzhen, 518060, P. R. China2National Center for Applied Mathematics Shenzhen (NCAMS), Shenzhen 518055, P. R. China3The Pazhou Lab, Guangzhou 510335, P. R. China
Jian Lu: Shenzhen Key Laboratory of Advanced Machine Learning and Applications, School of Mathematical Sciences, Shenzhen University, Shenzhen, 518060, P. R. China2National Center for Applied Mathematics Shenzhen (NCAMS), Shenzhen 518055, P. R. China3The Pazhou Lab, Guangzhou 510335, P. R. China

FRACTALS (fractals), 2024, vol. 32, issue 01, 1-15

Abstract: Salt-and-pepper noise consists of outlier pixel values which significantly impair image structure and quality. Multiparent fractal image coding (MFIC) methods substantially exploit image redundancy by utilizing multiple domain blocks to approximate the range block, partially compensating for the information loss caused by noise. Motivated by this, we propose two novel image restoration methods based on MFIC to remove salt-and-pepper noise. The first method integrates Huber M-estimation into MFIC, resulting in an improved anti-salt-and-pepper noise robust fractal coding approach. The second method incorporates MFIC into a total variation (TV) regularization model, including a data fidelity term, an MFIC term and a TV regularization term. An alternative iterative method based on proximity operator is developed to effectively solve the proposed model. Experimental results demonstrate that these two proposed approaches achieve significantly enhanced performance compared to traditional fractal coding methods.

Keywords: Multiparent Fractal Coding; Image Restoration; Robust Regression; Total Variation; Salt-and-pepper Noise (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0218348X24500129
Access to full text is restricted to subscribers

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:wsi:fracta:v:32:y:2024:i:01:n:s0218348x24500129

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0218348X24500129

Access Statistics for this article

FRACTALS (fractals) is currently edited by Tara Taylor

More articles in FRACTALS (fractals) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().