Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/139970
Title: SAR Image Denoising via Sparse Representation in Shearlet Domain Based on Continuous Cycle Spinning
Authors: Shuaiqi Liu;Ming Liu;Peifei Li;Jie Zhao;Zhihui Zhu;Xuehu Wang
Year: 2017
Publisher: IEEE
Abstract: How to suppress speckle noise effectively has become one of the key problems in remote sensing image processing. This problem also restricts the development of key technology severely, especially in military applications and so on. To overcome the shortcoming that the optimal solution of image denoising based on sparse representation does not have one-to-one mapping of the original signal space, in this paper, we propose a novel synthetic aperture radar (SAR) image denoising via sparse representation in Shearlet domain based on continuous cycle spinning. First, the Shearlet transform is applied to the noised SAR image. Second, a new optimal denoising model is constructed using the sparse representation model based on the cycle spinning theory. Finally, the alternate iteration algorithm is used to solve the optimal denoising model to obtain the denoised image. The experimental results show that the proposed method not only effectively suppresses the speckle noise and improves the peak signal-to-noise ratio of denoising SAR image, but also obviously improves the visual effect of the SAR image, especially by enhancing the texture of the SAR image.
URI: http://localhost/handle/Hannan/139970
volume: 55
issue: 5
More Information: 2985,
2992
Appears in Collections:2017

Files in This Item:
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7857084.pdf1.9 MBAdobe PDF
Title: SAR Image Denoising via Sparse Representation in Shearlet Domain Based on Continuous Cycle Spinning
Authors: Shuaiqi Liu;Ming Liu;Peifei Li;Jie Zhao;Zhihui Zhu;Xuehu Wang
Year: 2017
Publisher: IEEE
Abstract: How to suppress speckle noise effectively has become one of the key problems in remote sensing image processing. This problem also restricts the development of key technology severely, especially in military applications and so on. To overcome the shortcoming that the optimal solution of image denoising based on sparse representation does not have one-to-one mapping of the original signal space, in this paper, we propose a novel synthetic aperture radar (SAR) image denoising via sparse representation in Shearlet domain based on continuous cycle spinning. First, the Shearlet transform is applied to the noised SAR image. Second, a new optimal denoising model is constructed using the sparse representation model based on the cycle spinning theory. Finally, the alternate iteration algorithm is used to solve the optimal denoising model to obtain the denoised image. The experimental results show that the proposed method not only effectively suppresses the speckle noise and improves the peak signal-to-noise ratio of denoising SAR image, but also obviously improves the visual effect of the SAR image, especially by enhancing the texture of the SAR image.
URI: http://localhost/handle/Hannan/139970
volume: 55
issue: 5
More Information: 2985,
2992
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7857084.pdf1.9 MBAdobe PDF
Title: SAR Image Denoising via Sparse Representation in Shearlet Domain Based on Continuous Cycle Spinning
Authors: Shuaiqi Liu;Ming Liu;Peifei Li;Jie Zhao;Zhihui Zhu;Xuehu Wang
Year: 2017
Publisher: IEEE
Abstract: How to suppress speckle noise effectively has become one of the key problems in remote sensing image processing. This problem also restricts the development of key technology severely, especially in military applications and so on. To overcome the shortcoming that the optimal solution of image denoising based on sparse representation does not have one-to-one mapping of the original signal space, in this paper, we propose a novel synthetic aperture radar (SAR) image denoising via sparse representation in Shearlet domain based on continuous cycle spinning. First, the Shearlet transform is applied to the noised SAR image. Second, a new optimal denoising model is constructed using the sparse representation model based on the cycle spinning theory. Finally, the alternate iteration algorithm is used to solve the optimal denoising model to obtain the denoised image. The experimental results show that the proposed method not only effectively suppresses the speckle noise and improves the peak signal-to-noise ratio of denoising SAR image, but also obviously improves the visual effect of the SAR image, especially by enhancing the texture of the SAR image.
URI: http://localhost/handle/Hannan/139970
volume: 55
issue: 5
More Information: 2985,
2992
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7857084.pdf1.9 MBAdobe PDF