scholarly journals GF-3 SAR IMAGE DESPECKLING BASED ON THE IMPROVED NON-LOCAL MEANS USING NON-SUBSAMPLED SHEARLET TRANSFORM

2018 ◽  
Vol XLII-3 ◽  
pp. 1547-1551
Author(s):  
R. Shi ◽  
Z. Sun
Keyword(s):  
Imaging System ◽  
Sar Image ◽  
Sar Images ◽  
Edge Preserving ◽  
Shearlet Transform ◽  
Local Means ◽  
Non Local ◽  
Homogeneous Regions ◽  
Sparse Features ◽  
Image Details

GF-3 synthetic aperture radar (SAR) images are rich in information and have obvious sparse features. However, the speckle appears in the GF-3 SAR images due to the coherent imaging system and it hinders the interpretation of images seriously. Recently, Shearlet is applied to the image processing with its best sparse representation. A new Shearlet-transform-based method is proposed in this paper based on the improved non-local means. Firstly, the logarithmic operation and the non-subsampled Shearlet transformation are applied to the GF-3 SAR image. Secondly, in order to solve the problems that the image details are smoothed overly and the weight distribution is affected by the speckle, a new non-local means is used for the transformed high frequency coefficient. Thirdly, the Shearlet reconstruction is carried out. Finally, the final filtered image is obtained by an exponential operation. Experimental results demonstrate that, compared with other despeckling methods, the proposed method can suppress the speckle effectively in homogeneous regions and has better capability of edge preserving.

scholarly journals Synthetic-Aperture Radar Image Despeckling based on Improved Non-Local Means and Non-Subsampled Shearlet Transform

2020 ◽  
Vol 49 (3) ◽  
pp. 299-307
Author(s):  
Zengguo Sun ◽  
Rui Shi ◽  
Wei Wei
Keyword(s):  
Synthetic Aperture Radar Image ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Sar Images ◽  
Shearlet Transform ◽  
Local Means ◽  
New Type ◽  
Non Local ◽  
Homogeneous Regions ◽  
Transform Domains

When Synthetic-Aperture (SAR) image is transformed into wavelet domain and other transform domains, most of the coefficients of the image are small or zero. This shows that SAR image is sparse. However, speckle can be seen in SAR images. The non-local means is a despeckling algorithm, but it cannot overcome the speckle in homogeneous regions and it blurs edge details of the image. In order to solve these problems, an improved non-local means is suggested. At the same time, in order to better suppress the speckle effectively in edge regions, the non-subsampled Shearlet transform (NSST) is applied. By combining NSST with the improved non-local means, a new type of despeckling algorithm is proposed. Results show that the proposed algorithm leads to a satisfying performance for SAR images.

scholarly journals SAR Image De-Noising based on GNL-Means with Optimized Pixel-Wise Weighting in Non-Subsample Shearlet Domain

2016 ◽  
Vol 10 (1) ◽  
pp. 16 ◽  
Author(s):  
Shuaiqi Liu ◽  
Yu Zhang ◽  
Qi Hu ◽  
Ming Liu ◽  
Jie Zhao
Keyword(s):  
Remote Sensing ◽  
New Method ◽  
Sar Image ◽  
Important Research ◽  
Sar Images ◽  
Visual Effect ◽  
Research Issues ◽  
Shearlet Transform ◽  
Local Means ◽  
Non Local

SAR images have been widely used in many fields such as military and remote sensing. So the suppression of the speckle has been an important research issues. To improve the visual effect of non-local means, generalized non-local (GNL) means with optimized pixel-wise weighting is applied to shrink the coefficients of non-subsample Shearlet transform (NSST) of SAR image. The new method can optimize the weight of GNL, which not only improve the PSNR of de-noised image, but also can significantly enhance the visual effect of de-noising image.

scholarly journals Bankline detection of GF-3 SAR images based on shearlet

2021 ◽  
Vol 7 ◽  
pp. e611
Author(s):  
Zengguo Sun ◽  
Guodong Zhao ◽  
Marcin Woźniak ◽  
Rafał Scherer ◽  
Robertas Damaševičius
Keyword(s):  
Water Environment ◽  
Detection Algorithm ◽  
Morphological Processing ◽  
Map Matching ◽  
Sar Image ◽  
Sar Images ◽  
Multi Scale ◽  
Local Means ◽  
Active Imaging ◽  
Non Local

The GF-3 satellite is China’s first self-developed active imaging C-band multi-polarization synthetic aperture radar (SAR) satellite with complete intellectual property rights, which is widely used in various fields. Among them, the detection and recognition of banklines of GF-3 SAR image has very important application value for map matching, ship navigation, water environment monitoring and other fields. However, due to the coherent imaging mechanism, the GF-3 SAR image has obvious speckle, which affects the interpretation of the image seriously. Based on the excellent multi-scale, directionality and the optimal sparsity of the shearlet, a bankline detection algorithm based on shearlet is proposed. Firstly, we use non-local means filter to preprocess GF-3 SAR image, so as to reduce the interference of speckle on bankline detection. Secondly, shearlet is used to detect the bankline of the image. Finally, morphological processing is used to refine the bankline and further eliminate the false bankline caused by the speckle, so as to obtain the ideal bankline detection results. Experimental results show that the proposed method can effectively overcome the interference of speckle, and can detect the bankline information of GF-3 SAR image completely and smoothly.

scholarly journals Speckle noise suppression method using multi-azimuth SAR images.

2021 ◽  
Vol 2083 (3) ◽  
pp. 032053
Author(s):  
Yingru Shi ◽  
Yang Liu ◽  
Peili Xi ◽  
Wei Yang ◽  
Hongcheng Zeng
Keyword(s):  
Noise Suppression ◽  
Speckle Noise ◽  
Image Sequences ◽  
Sar Images ◽  
Radar Images ◽  
Local Means ◽  
Structural Details ◽  
Novel Method ◽  
Non Local ◽  
Suppression Method

Abstract Synthetic aperture radar images play an important role in military and civilian fields, but the presence of speckle noise has an impact on subsequent tasks such as target detection and target interpretation. With the development of multi-azimuth observation mode, the obtained multi-azimuth image sequences have high similarities. Therefore, combined with multi-azimuth image sequences, a novel method of SAR image speckle noise suppression based on clustering is proposed in this paper. In this method, multi-azimuth joint filtering framework based on two-level filtering is proposed, in which pre-filtering for single image and joint filtering based on Non-local Means algorithm for multi-azimuth image are used to suppress the noise. And k-means clustering is used to optimize the search area in the multi-azimuth joint filtering, so as to effectively suppress speckle noise while retaining structural details.

scholarly journals Optimal graph edge weights driven nlms with multi-layer residual compensation

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Fang Yang ◽  
Xin Chen ◽  
Li Chai
Keyword(s):  
Image Denoising ◽  
Graph Laplacian ◽  
Denoising Method ◽  
Local Means ◽  
Effective Performance ◽  
Non Local ◽  
Local Gradient ◽  
Laplacian Regularization ◽  
Image Details ◽  
Optimal Graph

AbstractNon-local Means (NLMs) play essential roles in image denoising, restoration, inpainting, etc., due to its simple theory but effective performance. However, when the noise increases, the denoising accuracy of NLMs decreases significantly. This paper further develop the NLMs-based denoising method to remove noise with less loss of image details. It is realized by embedding an optimal graph edge weights driven NLMs kernel into a multi-layer residual compensation framework. Unlike the patch similarity-based weights in the traditional NLMs filters, the edge weights derived from the optimal graph Laplacian regularization consider (1) the distance between the target pixel and the candidate pixel, (2) the local gradient and (3) the patch similarity. After defining the weights, the graph-based NLMs kernel is then put into a multi-layer framework. The corresponding primal and residual terms at each layer are finally fused with learned weights to recover the image. Experimental results show that our method is effective and robust, especially for piecewise smooth images.

scholarly journals SAR Image Segmentation Using Region Smoothing and Label Correction

2020 ◽  
Vol 12 (5) ◽  
pp. 803
Author(s):  
Ronghua Shang ◽  
Junkai Lin ◽  
Licheng Jiao ◽  
Yangyang Li
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Majority Voting ◽  
Sar Image ◽  
Application Process ◽  
Sar Images ◽  
Sar Image Segmentation ◽  
Segmentation Methods ◽  
The Difference ◽  
Homogeneous Regions

The traditional unsupervised image segmentation methods are widely used in synthetic aperture radar (SAR) image segmentation due to the simple and convenient application process. In order to solve the time-consuming problem of the common methods, an SAR image segmentation method using region smoothing and label correction (RSLC) is proposed. In this algorithm, the image smoothing results are used to approximate the results of the spatial information polynomials of the image. Thus, the segmentation process can be realized quickly and effectively. Firstly, direction templates are used to detect the directions at different coordinates of the image, and smoothing templates are used to smooth the edge regions according to the directions. It achieves the smoothing of the edge regions and the retention of the edge information. Then the homogeneous regions are presented indirectly according to the difference of directions. The homogeneous regions are smoothed by using isotropic operators. Finally, the two regions are fused for K-means clustering. The majority voting algorithm is used to modify the clustering results, and the final segmentation results are obtained. Experimental results on simulated SAR images and real SAR images show that the proposed algorithm outperforms the other five state-of-the-art algorithms in segmentation speed and accuracy.

Sign in / Sign up

Export Citation Format

Share Document