A Nonlocal SAR Image Denoising Algorithm Based on LLMMSE Wavelet Shrinkage

Parrilli, Sara; Poderico, Mariana; Angelino, Cesario Vincenzo; Verdoliva, Luisa

doi:10.1109/tgrs.2011.2161586

Cited by 622 publications

(432 citation statements)

References 43 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%

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%

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

Liu

2017

Remote Sensing

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“…Equivalent Number of Looks (ENL) [28,29]. ENL reflects the degree of speckle suppression, and the formula is as follows: (29) where E and var are the mean and the variance, respectively, of the image homogeneous area after speckle suppression.…”

Section: Evaluation Methodsmentioning

confidence: 99%

“…To further demonstrate the performance capabilities of each method, we obtained the ratio images, as mentioned in [28], as the pointwise ratio between the original SAR image and de-noised SAR images. Given a perfect de-noising, the ratio image should contain only speckle [25,28].…”

Section: Experiments On An Image With Added Speckle Noisementioning

confidence: 99%

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Speckle Suppression by Weighted Euclidean Distance Anisotropic Diffusion

Feng

Zhang

et al. 2018

Remote Sensing

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Abstract:To better reduce image speckle noise while also maintaining edge information in synthetic aperture radar (SAR) images, we propose a novel anisotropic diffusion algorithm using weighted Euclidean distance (WEDAD). Presented here is a modified speckle reducing anisotropic diffusion (SRAD) method, which constructs a new edge detection operator using weighted Euclidean distances. The new edge detection operator can adaptively distinguish between homogenous and heterogeneous image regions, effectively generate anisotropic diffusion coefficients for each image pixel, and filter each pixel at different scales. Additionally, the effects of two different weighting methods (Gaussian weighting and non-linear weighting) of de-noising were analyzed. The effect of different adjustment coefficient settings on speckle suppression was also explored. A series of experiments were conducted using an added noise image, GF-3 SAR image, and YG-29 SAR image. The experimental results demonstrate that the proposed method can not only significantly suppress speckle, thus improving the visual effects, but also better preserve the edge information of images.

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“…However, the smoothing of homogeneous areas and the preserving of edges are still not well balanced in these methods. The BM3D method is generally considered as state of art method in denoising of images and despeckling of SAR images [10]. The compressive sensing 3D (CS-3D) despeckling framework is comprised of three major steps; selection of subsets of pixels from SAR images, reconstruction of SAR image from each subset of pixels using CS theory, and statistical combining of multiple reconstructed images by employing selective 3D filtering.…”

Section: Introductionmentioning

confidence: 99%

RISAT-1 Image Despeckling using a Modified Undecimated BM3D Technique

Y¹,

Radhika²

2016

IJIGSP

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Abstract-In synthetic aperture radar (SAR) imaging, the transmitted microwave pulses from space born antenna interacts with ground objects and returned energy or back scattered energy will be collected to get backscattered image. In SAR image processing, a not anticipated noise (speckle noise) is added due to the coherent imaging system, which makes the image analysis troublesome. For better SAR image processing, the noise is to be removed or minimized in the begging stages of pre-processing and texture features are to be effectively maintained. The wavelet based Block Matching 3D (BM3D) method is normally considered as the state of art technique in the area of denoising of images. This method generally depends on up and down sampling conversion. In this paper, it is proposed a denoising technique which is independent on sampling conversion, so that texture features can be maintained, in which the speckle noise is reduced to the maximum extent.

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A Nonlocal SAR Image Denoising Algorithm Based on LLMMSE Wavelet Shrinkage

Cited by 622 publications

References 43 publications

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

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

Speckle Suppression by Weighted Euclidean Distance Anisotropic Diffusion

RISAT-1 Image Despeckling using a Modified Undecimated BM3D Technique

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