An effective weighted vector median filter for impulse noise reduction based on minimizing the degree of aggregation
Abstract Impulse noise is regarded as an outlier in the local window of an image. To detect noise, many proposed methods are based on aggregated distance, including spatially weighted aggregated distance, n nearest neighbour distance, local density, and angle‐weighted quaternion aggregated distance....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | IET Image Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ipr2.12023 |
| _version_ | 1797986131112886272 |
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| author | Xiangxi Meng Tongwei Lu Feng Min Tao Lu |
| author_facet | Xiangxi Meng Tongwei Lu Feng Min Tao Lu |
| author_sort | Xiangxi Meng |
| collection | DOAJ |
| description | Abstract Impulse noise is regarded as an outlier in the local window of an image. To detect noise, many proposed methods are based on aggregated distance, including spatially weighted aggregated distance, n nearest neighbour distance, local density, and angle‐weighted quaternion aggregated distance. However, these methods ignore the weight of each pixel or have limited adaptability. This study introduces the concept of degree of aggregation and proposes a weighting method to obtain the weight vector of the pixels by minimizing the degree of aggregation. The weight vector obtained gives larger components on the signal pixels than on the noisy pixels. Then it is fused with the aggregated distance to form a weighted aggregated distance that can reasonably characterise the noise and signal. The weighted aggregated distance, along with an adaptive segmentation method, can effectively detect the noise. To further enhance the effect of noise detection and removal, an adaptive selection strategy is incorporated to reduce the noise density in the local window. At last, noisy pixels detected are replaced with the weighted channel combination optimization values. The experimental results exhibit the validity of the proposed method by showing better performance in terms of both objective criteria and visual effects. |
| first_indexed | 2024-04-11T07:29:10Z |
| format | Article |
| id | doaj.art-de768dc1dd794a5ea09ac65b93b5bdf0 |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-04-11T07:29:10Z |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-de768dc1dd794a5ea09ac65b93b5bdf02022-12-22T04:36:59ZengWileyIET Image Processing1751-96591751-96672021-01-0115122823810.1049/ipr2.12023An effective weighted vector median filter for impulse noise reduction based on minimizing the degree of aggregationXiangxi Meng0Tongwei Lu1Feng Min2Tao Lu3School of Computer Science and Engineering Wuhan Institute of Technology Wuhan ChinaHubei Key Laboratory of Intelligent Robot (Wuhan Institute of Technology) Wuhan ChinaSchool of Computer Science and Engineering Wuhan Institute of Technology Wuhan ChinaHubei Key Laboratory of Intelligent Robot (Wuhan Institute of Technology) Wuhan ChinaAbstract Impulse noise is regarded as an outlier in the local window of an image. To detect noise, many proposed methods are based on aggregated distance, including spatially weighted aggregated distance, n nearest neighbour distance, local density, and angle‐weighted quaternion aggregated distance. However, these methods ignore the weight of each pixel or have limited adaptability. This study introduces the concept of degree of aggregation and proposes a weighting method to obtain the weight vector of the pixels by minimizing the degree of aggregation. The weight vector obtained gives larger components on the signal pixels than on the noisy pixels. Then it is fused with the aggregated distance to form a weighted aggregated distance that can reasonably characterise the noise and signal. The weighted aggregated distance, along with an adaptive segmentation method, can effectively detect the noise. To further enhance the effect of noise detection and removal, an adaptive selection strategy is incorporated to reduce the noise density in the local window. At last, noisy pixels detected are replaced with the weighted channel combination optimization values. The experimental results exhibit the validity of the proposed method by showing better performance in terms of both objective criteria and visual effects.https://doi.org/10.1049/ipr2.12023AlgebraOptical, image and video signal processingFiltering methods in signal processingOptimisation techniquesComputer vision and image processing techniquesOptimisation techniques |
| spellingShingle | Xiangxi Meng Tongwei Lu Feng Min Tao Lu An effective weighted vector median filter for impulse noise reduction based on minimizing the degree of aggregation IET Image Processing Algebra Optical, image and video signal processing Filtering methods in signal processing Optimisation techniques Computer vision and image processing techniques Optimisation techniques |
| title | An effective weighted vector median filter for impulse noise reduction based on minimizing the degree of aggregation |
| title_full | An effective weighted vector median filter for impulse noise reduction based on minimizing the degree of aggregation |
| title_fullStr | An effective weighted vector median filter for impulse noise reduction based on minimizing the degree of aggregation |
| title_full_unstemmed | An effective weighted vector median filter for impulse noise reduction based on minimizing the degree of aggregation |
| title_short | An effective weighted vector median filter for impulse noise reduction based on minimizing the degree of aggregation |
| title_sort | effective weighted vector median filter for impulse noise reduction based on minimizing the degree of aggregation |
| topic | Algebra Optical, image and video signal processing Filtering methods in signal processing Optimisation techniques Computer vision and image processing techniques Optimisation techniques |
| url | https://doi.org/10.1049/ipr2.12023 |
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