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SAR image despeckling with a multilayer perceptron neural network

International Journal of Digital Earth 2019年第3期12卷 354-374页

作者： Xiao Tang Lei Zhang Xiaoli Ding Department of Land Surveying and Geo-informatics The Hong Kong Polytechnic UniversityKowloonHong Kong

Speckle noise in synthetic-aperture radar (SAR) images severely hindersremote sensing applications;therefore, the appropriate removal ofspeckle noise is crucial. This paper elaborates on the multilayerperceptron (MLP) neural-network model for SAR image despeckling byusing a time series of SAR images. Unlike other filtering methods thatuse only a single radar intensity image to derive their parameters andfilter that single image, this method can be trained using archivedimages over an area of interest to self-learn the intensitycharacteristics of image patches and then adaptively determine theweights and thresholds by using a neural network for imagedespeckling. Several hidden layers are designed for feedforwardnetwork training, and back-propagation stochastic gradient descent isadopted to reduce the error between the target output and neuralnetwork output. The parameters in the network are automaticallyupdated in the training process. The greatest advantage of MLP is thatonce the despeckling parameters are determined, they can be used toprocess not only new images in the same area but also images incompletely different locations. Tests with images from TerraSAR-X inselected areas indicated that MLP shows satisfactory performance withrespect to noise reduction and edge preservation. The overall imagequality obtained using MLP was markedly higher than that obtainedusing numerous other filters. In comparison with other recentlydeveloped filters, this method yields a slightly higher image quality,and it demonstrates the powerful capabilities of computer learningusing SAR images, which indicate the promising prospect of applyingMLP to SAR image despeckling.

关键词： Multilayer perceptron synthetic aperture radar despeckling neural network

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despeckling Algorithm on Ultrasonic Image Using Adaptive Block-Based Singular Value Decomposition

Despeckling Algorithm on Ultrasonic Image Using Adaptive Blo...

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2007亚洲光电子会议电子成像与多媒体技术分会场

作者： Napa Sae-Bae Somkait Udomhunsakul King Mongkut’s Institute of Technology Ladkrabang Faculty of Engineering Information Engineering Department Bangkok Thailand

Speckle noise reduction is an important technique to enhance the quality of ultrasonic image. In this paper, a despeckling algorithm based on an adaptive block-based singular value decomposition filtering (BSVD) applied on ultrasonic images is presented. Instead of applying BSVD directly to ultrasonic image, we propose to apply BSVD on the noisy edge image version obtained from the difference between the logarithmic transformations of the original image and blur image version of its. The recovered image is performed by combining the speckle noise-free edge image with blur image version of its. Finally, exponential transformation is applied in order to get the reconstructed image. To evaluate our algorithm compared with well-know algorithms such as Lee filter, Kuan filter, Homomorphic Wiener filter, median filter and wavelet soft thresholding, four image quality measurements, which are Mean Square Error (MSE), Signal to MSE (S/MSE), Edge preservation ( ), and Correlation measurement ( ), are used. From the results, it clearly shows that the proposed algorithm outperforms other methods in terms of quantitative and subjective assessments.

关键词： Ultrasonic image SVD despeckling singular value thresholding speckle noise speckle denoising

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A Downsampled SAR-BM3D despeckling Approach for Single-Look SAR Images in High Resolution

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Journal of Computer and Communications 2016年第15期4卷 126-131页

作者： Wuchao Wang Xiaolin Liu Wenlong Zhang National University of Defense Technology Changsha China

SAR-BM3D is one of the state of the art despeckling algorithms for SAR images. However, when tackling with high resolution SAR images, it often has an unsatisfying despeckling performance in the homogeneous smooth regions, together with a high time complexity. In this paper, a novel downsampled SAR-BM3D despeckling approach combined with edge compensation is proposed. The proposed algorithm consists of two steps. First, despeckle the image which is a downsampled version of original image with SAR-BM3D. Then, compensate edges in each level when upsampling. This approach not only utilizes the good ability of feature preservation, but also improves performance of smoothing homogenous regions. When it comes to high resolution SAR images, the efficiency can be raised by six to seven times, compared to original SAR-BM3D. Experiments on simulated and real SAR images show that the proposed method reaches a high level in terms of visual quality and act more efficiently.

关键词： despeckling SAR-BM3D Downsampling High Resolution Synthetic Aperture Radar (SAR)

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Bayesian-Based Speckle Suppression for SAR Image Using Contourlet Transform

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Journal of Electronic Science and Technology of China 2008年第1期6卷 79-82页

作者： De-Xiang Zhang Qing-Wei Gao Xiao-Pei Wu School of Electronic Science andTechnology of Anhui University Hefei 230039 China Intelligent Computing and SignalProcessing of MOE Anhui University Hefei 230039 China

A novel and efficient speckle noise reduction algorithm based on Bayesian contourlet shrinkage using contourlet transform is proposed.First,we show the sub-band decompositions of SAR images using contourle transforms,which provides sparse representation at both spatial and directional resolutions.Then,a Bayesian contourlet shrinkage factor is applied to the decomposed data to estimate the best value for noise-free contourle coefficients.Experimental results show that compared with conventional wavelet despeckling algorithm,the proposed algorithm can achieve an excellent balance between suppresses speckle effectively and preserve image details,and the significant information of origina image like textures and contour details is well ma intained.

关键词： Bayesian shrinkage contourlet transform despeckling SAR image.

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