An Adaptive Algorithm for Image Denoising Based on Wavelet Transform

Guo Peng (guopeng111111@126.com), Yang Ping-xian (ypingx@163.com) and Wang Wei (wangzhiruiwangwei@163.com)
Additional contact information
Guo Peng: Sichuan University of Science & Engineering
Yang Ping-xian: Sichuan University of Science & Engineering
Wang Wei: Sichuan University of Science & Engineering

A chapter in 2012 International Conference on Information Technology and Management Science(ICITMS 2012) Proceedings, 2013, pp 575-587 from Springer

Abstract: Abstract In view of the traditional wavelet de-noising edge is easily destroyed, which causes the useful detail information of image drop-out problem, this article proposed one kind of algorithm that based on the wavelet transform image auto-adapted de-noising. Firstly, this algorithm carries on the piecemeal match to the image, constructs each similar block the data set; Secondly, it carries on the wavelet transform to it, and takes the noise variance iteration as the foundation; Finally, it makes auto-adapted de-noising processing separately with the soft and hard threshold function to the high or low frequency coefficient. The experimental result shows that after the improvement method applies in the image de-noising, can retain more detail information well, enhance the image peak signal-to-noise ratio (PSNR) and the visual effect has improved.

Keywords: Wavelet transform; Soft threshold function; Hard threshold function; PSNR; Detail information (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:sprchp:978-3-642-34910-2_67

Ordering information: This item can be ordered from
http://www.springer.com/9783642349102

DOI: 10.1007/978-3-642-34910-2_67

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla (sonal.shukla@springer.com) and Springer Nature Abstracting and Indexing (indexing@springernature.com).