Color Image Denoising Based on Guided Filter and Adaptive Wavelet Threshold
In the process of denoising color images, it is very important to enhance the edge and texture information of the images. Image quality can usually be improved by eliminating noise and enhancing contrast. Based on the adaptive wavelet threshold shrinkage algorithm and considering structural characte...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2017/5835020 |
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| _version_ | 1850166840153604096 |
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| author | Xin Sun Ning He Yu-Qing Zhang Xue-Yan Zhen Ke Lu Xiu-Ling Zhou |
| author_facet | Xin Sun Ning He Yu-Qing Zhang Xue-Yan Zhen Ke Lu Xiu-Ling Zhou |
| author_sort | Xin Sun |
| collection | DOAJ |
| description | In the process of denoising color images, it is very important to enhance the edge and texture information of the images. Image quality can usually be improved by eliminating noise and enhancing contrast. Based on the adaptive wavelet threshold shrinkage algorithm and considering structural characteristics on the basis of color image denoising, this paper describes a method that further enhances the edge and texture details of the image using guided filtering. The use of guided filtering allows edge details that cannot be discriminated in grayscale images to be preserved. The noisy image is decomposed into low-frequency and high-frequency subbands using discrete wavelets, and the contraction function of threshold shrinkage is selected according to the energy in the vicinity of the wavelet coefficients. Finally, the edge and texture information of the denoised color image are enhanced by guided filtering. When the guiding image is the original noiseless image itself, the guided filter can be used as a smoothing operator for preserving edges, resulting in a better effect than bilateral filtering. The proposed method is compared with the adaptive wavelet threshold shrinkage denoising algorithm and the bilateral filtering algorithm. Experimental results show that the proposed method achieves superior color image denoising compared to these conventional techniques. |
| format | Article |
| id | doaj-art-de1e1ebf2e09414fbc847250a344affe |
| institution | OA Journals |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-de1e1ebf2e09414fbc847250a344affe2025-08-20T02:21:20ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322017-01-01201710.1155/2017/58350205835020Color Image Denoising Based on Guided Filter and Adaptive Wavelet ThresholdXin Sun0Ning He1Yu-Qing Zhang2Xue-Yan Zhen3Ke Lu4Xiu-Ling Zhou5Smart City College, Beijing Union University, Beijing 100101, ChinaSmart City College, Beijing Union University, Beijing 100101, ChinaBeijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, ChinaBeijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, ChinaUniversity of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, ChinaBeijing City University, Beijing 100083, ChinaIn the process of denoising color images, it is very important to enhance the edge and texture information of the images. Image quality can usually be improved by eliminating noise and enhancing contrast. Based on the adaptive wavelet threshold shrinkage algorithm and considering structural characteristics on the basis of color image denoising, this paper describes a method that further enhances the edge and texture details of the image using guided filtering. The use of guided filtering allows edge details that cannot be discriminated in grayscale images to be preserved. The noisy image is decomposed into low-frequency and high-frequency subbands using discrete wavelets, and the contraction function of threshold shrinkage is selected according to the energy in the vicinity of the wavelet coefficients. Finally, the edge and texture information of the denoised color image are enhanced by guided filtering. When the guiding image is the original noiseless image itself, the guided filter can be used as a smoothing operator for preserving edges, resulting in a better effect than bilateral filtering. The proposed method is compared with the adaptive wavelet threshold shrinkage denoising algorithm and the bilateral filtering algorithm. Experimental results show that the proposed method achieves superior color image denoising compared to these conventional techniques.http://dx.doi.org/10.1155/2017/5835020 |
| spellingShingle | Xin Sun Ning He Yu-Qing Zhang Xue-Yan Zhen Ke Lu Xiu-Ling Zhou Color Image Denoising Based on Guided Filter and Adaptive Wavelet Threshold Applied Computational Intelligence and Soft Computing |
| title | Color Image Denoising Based on Guided Filter and Adaptive Wavelet Threshold |
| title_full | Color Image Denoising Based on Guided Filter and Adaptive Wavelet Threshold |
| title_fullStr | Color Image Denoising Based on Guided Filter and Adaptive Wavelet Threshold |
| title_full_unstemmed | Color Image Denoising Based on Guided Filter and Adaptive Wavelet Threshold |
| title_short | Color Image Denoising Based on Guided Filter and Adaptive Wavelet Threshold |
| title_sort | color image denoising based on guided filter and adaptive wavelet threshold |
| url | http://dx.doi.org/10.1155/2017/5835020 |
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