SAR Image Despeckling Research Articles

Amplitude vs intensity Bayesian despeckling in the wavelet domain for SAR images

In this paper, the problem of despeckling SAR images when the input data is either an intensity or an amplitude signal is revisited. State-of-the-art despeckling methods based on Bayesian estimators in the wavelet domain, recently proposed in the literature, are taken into consideration. First, how these methods proposed for one format (e.g., intensity) can be adapted to the other format (e.g., amplitude) is investigated. Second, the performance of such algorithms in both cases is analyzed. Experimental results carried out on simulated speckled images and on true SAR data are presented and discussed in order to assess the best strategy. From these results, it can be observed that filtering in the amplitude domain yields better performances in terms of objective quality indexes, such as preservation of structural details, as well as in terms of visual inspection of the filtered SAR data.

Stationary-Wavelet-Based Despeckling of SAR Images Using Two-Sided Generalized Gamma Models

In this letter, a stationary-wavelet-based despeckling algorithm based on the two-sided generalized gamma distribution (GΓD) model is proposed. We first introduce the two-sided GΓD as a flexible and efficient model for the wavelet coefficients of logarithmically transformed synthetic aperture radar intensity or amplitude. The strength of the model is highlighted in terms of its fit to the data, its low computational cost, and the ease of parameter estimation. By empirical results, we then motivate the GΓD as model for the wavelet coefficients of the noise-free signal. The GΓD model parameters are estimated with moment methods, using both absolute central moments for the wavelet coefficients of the noisy signal and the noise. Finally, we exploit the prior information contained in the model by designing a Bayesian maximum a posteriori estimator for estimating the noise-free wavelet coefficients. Experimental results demonstrate the superiority of our method in terms of simultaneously reducing speckle and preserving structural details.

Correction to “SAR Image Despeckling Based on Nonsubsampled Shearlet Transform” [Jun 12 809-823

We regret that the PSNRs of our method in Table III in the above titled paper (ibid., vol. 5, no. 3, pp. 809-823, Jun. 2012), were left out. The correct Table III is presented here.

Despeckling of SAR Images by Optimizing Averaged Power Spectral Value in Curvelet Domain

Despeckling of SAR Images by Optimizing Averaged Power Spectral Value in Curvelet Domain

SAR Image Despeckling Using Bayesian Nonlocal Means Filter With Sigma Preselection

Bayesian nonlocal (NL) (BNL) means filter, as an extension of the NL means algorithm, provides a general framework for image denoising when dealing with different noise. However, this approach makes a strong assumption that image patch itself provides a good approximation on the true parameter, which leads to the bias problem particularly under serious speckle noise. Another disadvantage of the BNL filter is that the commonly used patch preselection method cannot effectively exclude the outliers. In this letter, a new form of the BNL filter is presented for the purpose of synthetic aperture radar image despeckling, which incorporates the technique of sigma filter to cope with the bias problem. In addition, pixel preselection is adopted based on the refined sigma range, which greatly contributes to the preservation of the image details such as edges, texture, and the strong reflective scatters. Experimental results illustrate that the proposed BNL filter reaches the state-of-the-art performance on both the visual quality and evaluation indexes.

SAR Image Despeckling Based on Bivariate Threshold Function in NSCT Domain

该文根据非下采样Contourlet分解系数与其父系数之间的相关性，给出非高斯双变量分布模型，应用贝叶斯估值理论推导得到该模型相应的非线性双变量阈值函数。综合SAR图像非对数加性模型和双变量阈值函数，提出基于双变量模型的非下采样Contourlet变换域SAR图像相干斑抑制方法(SNSCTBI)。实验通过对幅度格式和强度格式的SAR图像做相干斑抑制，结果表明该文算法很好地保持了原始图像的辐射特性，有效抑制了同质区域的相干斑，同时边缘等纹理信息保持清晰。

An Approach for SAR Image Despeckling Based on 2D-Wavelet Transform and ICA

该文给出了一种基于离散小波变换和独立分量分析的SAR图像斑点噪声抑制方法。首先利用小波变换对图像进行分解,然后将分解出的各部分子图像分别进行独立分量分析,提取出相应的独立源,去除噪声分量,最后依次进行ICA重构和小波重构。该文还同时比较了采用不同小波基函数时斑点噪声的抑制效果,研究了它们对斑点抑制的影响。对MSTAR实测SAR图像的实验结果表明该方法能够有效地抑制图像中的斑点噪声,且在性能上优于ICA和Lee滤波方法。

SAR IMAGE DESPECKLING BASED ON ADAPTIVE WINDOW AND SHAPE ADAPTIVE-DISCRETE WAVELET TRANSFORM

By considering the prior knowledge of SAR image in spatial domain and the property of coefficients in wavelet domain,a novel algorithm of SAR image despeckling was presented.An adaptive window was constructed and a uniform region for every pixel of SAR image was found by using local polynomial approximation-intersection of confidence intervals(LPA-ICI).Hard-threshold shrinkage despeckling was implemented with fast shape adaptive discrete wavelet transform proposed in this paper.At last,many despeckled samples were fused into a final despeckled SAR image according to the sparsity of regions.Experiments show that the algorithm proposed here has advanced despecking performance.Especially,reconstructed image has no unpleasant ringing artifacts,and it efficiently reserves point targets of original SAR image.

Bayesian despeckling of SAR images based on the Membrane MRF prior model

Bayesian despeckling of SAR images based on the Membrane MRF prior model

Segmentation-Based MAP Despeckling of SAR Images in the Undecimated Wavelet Domain

In this paper, a novel despeckling algorithm based on undecimated wavelet decomposition and maximum <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">posteriori</i> estimation is proposed. Such a method represents an improvement with respect to the filter presented by the authors, and it is based on the same conjecture that the probability density functions (pdfs) of the wavelet coefficients follow a generalized Gaussian (GG) distribution. However, the approach introduced here presents two major novelties: 1) theoretically exact expressions for the estimation of the GG parameters are derived: such expressions do not require further assumptions other than the multiplicative model with uncorrelated speckle, and hold also in the case of a strongly correlated reflectivity; 2) a model for the classification of the wavelet coefficients according to their texture energy is introduced. This model allows us to classify the wavelet coefficients into classes having different degrees of heterogeneity, so that <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hoc</i> estimation approaches can be devised for the different sets of coefficients. Three different implementations, characterized by different approaches for incorporating into the filtering procedure the information deriving from the segmentation of the wavelet coefficients, are proposed. Experimental results, carried out on both artificially speckled images and true synthetic aperture radar images, demonstrate that the proposed filtering approach outperforms the previous filters, irrespective of the features of the underlying reflectivity.

Multiresolution MAP Despeckling of SAR Images Based on Locally Adaptive Generalized Gaussian pdf Modeling

In this paper, a new despeckling method based on undecimated wavelet decomposition and maximum a posteriori MIAP) estimation is proposed. Such a method relies on the assumption that the probability density function (pdf) of each wavelet coefficient is generalized Gaussian (GG). The major novelty of the proposed approach is that the parameters of the GG pdf are taken to be space-varying within each wavelet frame. Thus, they may be adjusted to spatial image context, not only to scale and orientation. Since the MAP equation to be solved is a function of the parameters of the assumed pdf model, the variance and shape factor of the GG function are derived from the theoretical moments, which depend on the moments and joint moments of the observed noisy signal and on the statistics of speckle. The solution of the MAP equation yields the MAP estimate of the wavelet coefficients of the noise-free image. The restored SAR image is synthesized from such coefficients. Experimental results, carried out on both synthetic speckled images and true SAR images, demonstrate that MAP filtering can be successfully applied to SAR images represented in the shift-invariant wavelet domain, without resorting to a logarithmic transformation.

A Comment on “Homomorphic Wavelet-Based Statistical Despeckling of SAR Images”

Explicit expressions are derived for the cumulants and the cumulant-generating function of the log-transformed radar cross section considered by Solbo and Eltoft. Some computational issues are also discussed

Homomorphic wavelet-based statistical despeckling of SAR images

In this paper, we introduce the homomorphic /spl Gamma/-WMAP (wavelet maximum a posteriori) filter, a wavelet-based statistical speckle filter equivalent to the well known /spl Gamma/-MAP filter. We perform a logarithmic transformation in order to make the speckle contribution additive and statistically independent of the radar cross section. Further, we propose to use the normal inverse Gaussian (NIG) distribution as a statistical model for the wavelet coefficients of both the reflectance image and the noise image. We show that the NIG distribution is an excellent statistical model for the wavelet coefficients of synthetic aperture radar images, and we present a method for estimating the parameters. We compare the homomorphic /spl Gamma/-WMAP filter with the /spl Gamma/-MAP filter and and the recently introduced /spl Gamma/-WMAP filter, which are both based on the same statistical assumptions. The homomorphic /spl Gamma/-WMAP filter is shown to have better performance with regard to smoothing homogeneous regions. It may in some cases introduce a small bias, but in our studies it is always less than that introduced by the /spl Gamma/-MAP filter. Further, the speckle removed by the homomorphic /spl Gamma/-WMAP filter has statistics closer to the theoretical model than the speckle contribution removed with the other filters.