scholarly journals Strip-Map SAR Image Formulation Based on the Modified Alternating Split Bregman Method

2021 ◽  
Vol 13 (21) ◽  
pp. 4231
Author(s):  
Fangfang Shen ◽  
Xuyang Chen ◽  
Yanming Liu ◽  
Yaocong Xie ◽  
Xiaoping Li
Keyword(s):  
Superior Performance ◽  
Sar Image ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Tv Regularization ◽  
Regularization Problem ◽  
Large Measurement ◽  
Contour Information ◽  
Synthetic Aperture Radars ◽  
High Computational Efficiency

Conventional compressive sensing (CS)-based imaging methods allow images to be reconstructed from a small amount of data, while they suffer from high computational burden even for a moderate scene. To address this problem, this paper presents a novel two-dimensional (2D) CS imaging algorithm for strip-map synthetic aperture radars (SARs) with zero squint angle. By introducing a 2D separable formulation to model the physical procedure of the SAR imaging, we separate the large measurement matrix into two small ones, and then the induced algorithm can deal with 2D signal directly instead of converting it into 1D vector. As a result, the computational load can be reduced significantly. Furthermore, thanks to its superior performance in maintaining contour information, the gradient space of the SAR image is exploited and the total variation (TV) constraint is incorporated to improve resolution performance. Due to the non-differentiable property of the TV regularizer, it is difficult to directly solve the induced TV regularization problem. To overcome this problem, an improved split Bregman method is presented by formulating the TV minimization problem into a sequence of unconstrained optimization problem and Bregman updates. It yields an accurate and simple solution. Finally, the synthesis and real experiment results demonstrate that the proposed algorithm remains competitive in terms of high resolution and high computational efficiency.

scholarly journals Fast Total-Variation Image Deconvolution with Adaptive Parameter Estimation via Split Bregman Method

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Chuan He ◽  
Changhua Hu ◽  
Wei Zhang ◽  
Biao Shi ◽  
Xiaoxiang Hu
Keyword(s):  
Total Variation ◽  
Regularization Parameter ◽  
Difficult Problem ◽  
Image Deconvolution ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Edge Preservation ◽  
Adaptive Parameter ◽  
Tv Regularization ◽  
Blurred Image

The total-variation (TV) regularization has been widely used in image restoration domain, due to its attractive edge preservation ability. However, the estimation of the regularization parameter, which balances the TV regularization term and the data-fidelity term, is a difficult problem. In this paper, based on the classical split Bregman method, a new fast algorithm is derived to simultaneously estimate the regularization parameter and to restore the blurred image. In each iteration, the regularization parameter is refreshed conveniently in a closed form according to Morozov’s discrepancy principle. Numerical experiments in image deconvolution show that the proposed algorithm outperforms some state-of-the-art methods both in accuracy and in speed.

scholarly journals Fast Split Bregman Based Deconvolution Algorithm for Airborne Radar Imaging

2020 ◽  
Vol 12 (11) ◽  
pp. 1747 ◽  
Author(s):  
Yin Zhang ◽  
Qiping Zhang ◽  
Yongchao Zhang ◽  
Jifang Pei ◽  
Yulin Huang ◽  
...  
Keyword(s):  
Real Time ◽  
Computing Time ◽  
Real Data ◽  
Radar Imaging ◽  
Matrix Inversion ◽  
Airborne Radar ◽  
Split Bregman ◽  
Regularization Problem ◽  
Azimuth Resolution ◽  
Split Bregman Algorithm

Deconvolution methods can be used to improve the azimuth resolution in airborne radar imaging. Due to the sparsity of targets in airborne radar imaging, an L 1 regularization problem usually needs to be solved. Recently, the Split Bregman algorithm (SBA) has been widely used to solve L 1 regularization problems. However, due to the high computational complexity of matrix inversion, the efficiency of the traditional SBA is low, which seriously restricts its real-time performance in airborne radar imaging. To overcome this disadvantage, a fast split Bregman algorithm (FSBA) is proposed in this paper to achieve real-time imaging with an airborne radar. Firstly, under the regularization framework, the problem of azimuth resolution improvement can be converted into an L 1 regularization problem. Then, the L 1 regularization problem can be solved with the proposed FSBA. By utilizing the low displacement rank features of Toeplitz matrix, the proposed FSBA is able to realize fast matrix inversion by using a Gohberg–Semencul (GS) representation. Through simulated and real data processing experiments, we prove that the proposed FSBA significantly improves the resolution, compared with the Wiener filtering (WF), truncated singular value decomposition (TSVD), Tikhonov regularization (REGU), Richardson–Lucy (RL), iterative adaptive approach (IAA) algorithms. The computational advantage of FSBA increases with the increase of echo dimension. Its computational efficiency is 51 times and 77 times of the traditional SBA, respectively, for echoes with dimensions of 218 × 400 and 400 × 400 , optimizing both the image quality and computing time. In addition, for a specific hardware platform, the proposed FSBA can process echo of greater dimensions than traditional SBA. Furthermore, the proposed FSBA causes little performance degradation, when compared with the traditional SBA.

Split Bregman method based level set formulations for segmentation and correction with application to MR images and color images

2019 ◽  
Vol 57 ◽  
pp. 50-67 ◽  
Author(s):  
Yunyun Yang ◽  
Dongcai Tian ◽  
Wenjing Jia ◽  
Xiu Shu ◽  
Boying Wu
Keyword(s):  
Level Set ◽  
Color Images ◽  
Mr Images ◽  
Split Bregman Method ◽  
Split Bregman

scholarly journals Fast Algorithms for the Anisotropic LLT Model in Image Denoising

2011 ◽  
Vol 1 (3) ◽  
pp. 264-283 ◽  
Author(s):  
Zhi-Feng Pang ◽  
Li-Lian Wang ◽  
Yu-Fei Yang
Keyword(s):  
Convergence Rate ◽  
Image Denoising ◽  
Projection Method ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Minimization Problems ◽  
Iterative Shrinkage ◽  
Speed Up ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding

AbstractIn this paper, we propose a new projection method for solving a general minimization problems with twoL1-regularization terms for image denoising. It is related to the split Bregman method, but it avoids solving PDEs in the iteration. We employ the fast iterative shrinkage-thresholding algorithm (FISTA) to speed up the proposed method to a convergence rateO(k−2). We also show the convergence of the algorithms. Finally, we apply the methods to the anisotropic Lysaker, Lundervold and Tai (LLT) model and demonstrate their efficiency.

L p Regularization for Bioluminescence Tomography Based on the Split Bregman Method

2016 ◽  
Vol 18 (6) ◽  
pp. 830-837 ◽  
Author(s):  
Yifang Hu ◽  
Jie Liu ◽  
Chengcai Leng ◽  
Yu An ◽  
Shuang Zhang ◽  
...  
Keyword(s):  
Split Bregman Method ◽  
Split Bregman ◽  
Bioluminescence Tomography
Sign in / Sign up

Export Citation Format

Share Document