Title
Significance driven inverse distance weighted filter to restore impulsive noise corrupted X-ray image
Abstract
This paper presents a novel significance driven inverse distance weighted (SDIDW) filter for the impulsive noise removal in the X-ray images. The proposed SDIDW filter restores the noisy pixel using minimum number of nearest noise-free pixels to achieve good estimation while exhibiting low computational complexity. In the proposed filter, higher priority (weight) is given to nearest pixels compared to distant pixels and only sufficient nearest noise free pixels are determined to estimate the value of noisy pixel. A high level analysis of the computation complexity at varying noise density is done which shows that proposed SDIDW filter provides significant reduction in computation complexity over the adaptive median filters. Finally, the performance of the proposed filter is evaluated and compared over the state-of-the-art impulse noise removal techniques for varying noise density (wide range 10–90% and very high noise density range 91–99%). The experimental results on medical images demonstrate significant improvement in filtered images quality by the proposed filter over the state-of-the-art filters at each sample of noise density with small computational complexity.
Year
DOI
Venue
2022
10.1007/s12652-021-02962-y
Journal of Ambient Intelligence and Humanized Computing
Keywords
DocType
Volume
Salt-and-pepper noise, Median filter, Mean filter, Non-linear filter, Image processing
Journal
13
Issue
ISSN
Citations 
4
1868-5137
0
PageRank 
References 
Authors
0.34
21
3
Name
Order
Citations
PageRank
Bharat Garg100.68
Prashant Singh Rana200.68
Vijaypal Singh Rathor300.34