SAR image de-noising based on non-local similar block matching in NSST domain

Hu, Shaohai; Ma, Xiaole; Liu, Shuaiqi; Yang, Dongsheng

doi:10.1109/icsp.2016.7877947

Cited by 5 publications

(7 citation statements)

References 12 publications

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“…Then, we applied the state-of-the-art image de-noising methods to them, such as the enhanced Lee filter (Lee) [29], shearlet (ST) [7], SAR-BM3D [30], K-SVD [25], iterative nonlocal sparse model (It-NSM) [31], and our method. In the part of the sparse model in It-NSM, we adopted K-SVD.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Finally, the de-noised image can be obtained by the inverse transform. For example, SAR image de-noising based on non-local similar block matching in NSST domain in [7], synthetic aperture radar image de-noising based shearlet transform using the context-based model in [8], and scattering-based SAR-Block Matching 3D (SARBM3D) [9]. Common transforms include Fourier transform, discrete cosine transform (DCT), wavelet and multi-scale multi-directional transform [10].…”

Section: Introductionmentioning

confidence: 99%

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SAR Image De-Noising Based on Shift Invariant K-SVD and Guided Filter

Liu

2017

Remote Sensing

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Finding a way to effectively suppress speckle in SAR images has great significance. K-means singular value decomposition (K-SVD) has shown great potential in SAR image de-noising. However, the traditional K-SVD is sensitive to the position and phase of the characteristics in the image, and the de-noised image by K-SVD has lost some detailed information of the original image. In this paper, we present one new SAR image de-noising method based on shift invariant K-SVD and guided filter. The whole method consists of two steps. The first deals mainly with the noisy image with shift invariant K-SVD and obtaining the initial de-noised image. In the second step, we do the guided filtering for the initial de-noised image. Finally, we can recover the final de-noised image. Experimental results show that our method not only has better visual effects and objective evaluation, but can also save more detailed information such as image edge and texture when de-noising SAR images. The presented shift invariant K-SVD can be widely used in image processing, such as image fusion, edge detection and super-resolution reconstruction.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SAR Image De-Noising Based on Shift Invariant K-SVD and Guided Filter

Liu

2017

Remote Sensing

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“…Since the noise and noise-free image is independent of each other [1], the model of the residual image in the high-frequency components can be simplified as Eq.(8).…”

Section: The Residual Image De-noising In St Domainmentioning

confidence: 99%

“…Nevertheless, the speckle noise caused by its coherent imaging mechanism, has a serious impact on the follow-up work like image enhancement and recovery. Thus, how to suppress even eliminate the speckle noise effectively, has become one of the main tasks in the SAR image study [1].…”

Section: Introductionmentioning

confidence: 99%

“…While transform domain methods usually adopt some orthogonal basis functions to represent the noisy image and obtain different frequency sub-band coefficients; the image de-noising can be realized by processing those coefficients. The common multi-scale geometric transforms [1] which are widely used in the speckle suppressing are dual tree complex wavelet transform (DTCWT), Contourlet, Shearlet etc. Since the image contains many different kinds of characteristics to express information, if we only use the fixed orthogonal basis function, which can hardly represent all the characteristics of the image, the noise and real information cannot be completely separated [3].…”

Section: Introductionmentioning

confidence: 99%

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SAR Image De-noising Based on Residual Image Fusion and Sparse Representation

2019

KSII TIIS

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Since the birth of Synthetic Aperture Radar (SAR), it has been widely used in the military field and so on. However, the existence of speckle noise makes a good deal inconvenience for the subsequent image processing. The continuous development of sparse representation (SR) opens a new field for the speckle suppressing of SAR image. Although the SR de-noising may be effective, the over-smooth phenomenon still has bad influence on the integrity of the image information. In this paper, one novel SAR image de-noising method based on residual image fusion and sparse representation is proposed. Firstly we can get the similar block groups by the non-local similar block matching method (NLS-BM). Then SR de-noising based on the adaptive K-means singular value decomposition (K-SVD) is adopted to obtain the initial de-noised image and residual image. The residual image is processed by Shearlet transform (ST), and the corresponding de-noising methods are applied on it. Finally, in ST domain the low-frequency and high-frequency components of the initial de-noised and residual image are fused respectively by relevant fusion rules. The final de-noised image can be recovered by inverse ST. Experimental results show the proposed method can not only suppress the speckle effectively, but also save more details and other useful information of the original SAR image, which could provide more authentic and credible records for the follow-up image processing.

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