EconPapers    
Economics at your fingertips  
 

An adaptive median filtering of visual product image based on gradient direction information

Kai Liu

International Journal of Product Development, 2022, vol. 26, issue 1/2/3/4, 206-215

Abstract: In order to overcome the problems of long filtering process, low signal-to-noise ratio of output results and low integrity of image information in traditional image median filtering methods, a new research method of visual product image adaptive median filtering based on gradient direction information is proposed in this paper. Based on the digital representation of the visual product image, the gradient direction information method is used to extract the noise information in the visual product image, so as to improve the quality of image filtering. Finally, the adaptive median filtering of visual product image is completed by processing the median and extreme values of visual product image. The simulation results show that the filtering process of this method takes 0.25-0.45 min, the signal-to-noise ratio can reach 85 dB, and the integrity of image information varies from 97.5% to 98.2%, which proves that it effectively realises the design expectation.

Keywords: gradient direction information; visual product image; adaptive median filter; noise extraction. (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=125373 (text/html)
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:ids:ijpdev:v:26:y:2022:i:1/2/3/4:p:206-215

Access Statistics for this article

More articles in International Journal of Product Development from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().

 
Page updated 2025-03-19
Handle: RePEc:ids:ijpdev:v:26:y:2022:i:1/2/3/4:p:206-215