Speckle noise is found in synthetic aperture radar (SAR) images and can affect visualization and analysis. A novel stochastic texture-based algorithm is proposed to suppress speckle noise while preserving the underlying structural and texture detail. Based on a sorted local texture model and a Fisher-Tippett logarithmic-space speckle distribution model, a Monte Carlo texture likelihood sampling strategy is proposed to estimate the true signal. The algorithm is compared to six other classic and state-of-the-art despeckling techniques. The comparison is performed both on synthetic noisy images added and on actual SAR images. Using peak signal-to-noise ratio, contrast-to-noise ratio, and structural similarity index as image quality metrics, the proposed algorithm shows strong despeckling performance when compared to existing despeckling algorithms.

Despeckling of Synthetic Aperture Radar Images Using Monte Carlo Texture Likelihood Sampling

In this paper, a bilateral filter is proposed for despeckling Synthetic Aperture Radar (SAR) images with an adaptive estimation of the domain and range parameter. The method applies a block-based approach to perform Laplacian Data masking and gradient operation for each pixel in the image utilizing its local neighbors. The range parameter is then adapted to the noise level estimated from the corresponding edge removed image. On the other hand, the domain parameter is adapted according to coefficient of variation of local neighboring pixels of the block. Experiments performed on synthetically-speckled images show that the proposed method outperforms several existing methods for despeckling in terms of the peak signal-to-noise ratio, structural similarity and edge preservation indices and visual quality. For real SAR images, it preserves the important details of the image while suppresses the speckle in homogeneous regions considerably.

Despeckling SAR images with an adaptive bilateral filter

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Sea-Ice Detection from RADARSAT Images by Gamma-based Bilateral Filtering

The goal of the present research is to despeckle SAR images, which is critical for segmentation and target recognition in satellite SAR images. When a despeckling algorithm is applied to a SAR image, important information such as the edges, corners, textures, and object parts will degrade. Curvelet transform is a recently proposed form of multi-scale analysis that achieves better performance of wavelet and Gabor transforms in edge and curve detection. This is a geometric transform that is useful for SAR image processing. For unsupervised texture images, segmentation is different and distinct from the textures, so the textures at the boundary noises will disappear. Curvelet transform has produced good results in the detection of curved edges with higher accuracy in finding the orientation than wavelet transforms. The present study uses fast discrete curvelet transform (FDCT) based on wresting and uses unsupervised adaptive threshold learning to develop a new despeckling algorithm for SAR images. In the proposed algorithm, each segment of the SAR image can be learned for selection of its adaptive threshold. Simulation results demonstrate that the proposed algorithm performs better than similar methods.

Despeckling algorithm for remote sensing synthetic aperture radar images using multi-scale curvelet transform

In this paper, we propose a new despeckling and super resolution (SR) approach for synthetic aperture radar (SAR) applications. In our method, we transfers the speckled low resolution SAR image to gradient domain and extract all of edges with their sharpness. Then, using Zernike Moments and also Bootstrapping approach we obtains the new values for edges sharpness. By these new edges sharpness and after using the modified gradient field transformation, image reconstruction leads to noise-free high resolution images with better quality than traditional methods.

Gradient based SAR image despeckling and super resolution using Zernike Moments and Bootstrapping

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which requires despeckle before many other image processing and analysis applications. In this paper, a new speckle reduction method based on wavelet and bilateral filtering is proposed. Bilatreal filtering is applied to the low frequency subbands of a signal obtained by wavelet transform. The new method preserves edges and protect details. Experimental results tested on real SAR images demonstrate the performance of the proposed method.

Bibo Lu

Papers

Speckle Reduction with Multiresolution Bilateral Filtering for SAR Image