An algorithm for multi-sensor image fusion using maximum a posteriori and nonsubsampled contourlet transform

Anandhi, D.; Valli, S.

doi:10.1016/j.compeleceng.2017.04.002

Cited by 33 publications

(12 citation statements)

References 18 publications

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“…Dictionary learning will be discussed in detail in Section 2.1. When performing sparse coding by orthogonal matching pursuit (OMP) [30] in this paper, Equation (1) can be replaced by Equation (2)…”

Section: Sparse Representationmentioning

confidence: 99%

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Remote Sensing Image Fusion Based on Sparse Representation and Guided Filtering

Liu

et al. 2019

Electronics

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In this paper, a remote sensing image fusion method is presented since sparse representation (SR) has been widely used in image processing, especially for image fusion. Firstly, we used source images to learn the adaptive dictionary, and sparse coefficients were obtained by sparsely coding the source images with the adaptive dictionary. Then, with the help of improved hyperbolic tangent function (tanh) and l 0 − max , we fused these sparse coefficients together. The initial fused image can be obtained by the image fusion method based on SR. To take full advantage of the spatial information of the source images, the fused image based on the spatial domain (SF) was obtained at the same time. Lastly, the final fused image could be reconstructed by guided filtering of the fused image based on SR and SF. Experimental results show that the proposed method outperforms some state-of-the-art methods on visual and quantitative evaluations.

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Section: Sparse Representationmentioning

confidence: 99%

“…Through image fusion, we can obtain one composite image, which contains more special features, and can provide more useful information. As a powerful tool for image processing, image fusion covers broad range of areas [2,3], such as computer vision, remote sensing, and so on [4].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Fusion Based on Sparse Representation and Guided Filtering

Liu

et al. 2019

Electronics

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“…To obtain good noise-free and edge-preserved SAR images, this work uses non-Gaussian NSCT model coefficients using Rayleigh and Laplacian statistical prior distributions [15]. The main contributions of this proposed work are stated as follows:…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Approach to Despeckle SAR Images

Valli¹,

Anandhi²

2018

RADIOENGINEERING

Self Cite

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Synthetic Aperture Radar (SAR) image processing plays a vital role in observing the earth and in understanding its varied features. A SAR image contains edges and shapes hidden by speckle noise. Therefore, despeckling is essential for subsequent feature extraction and classification. This paper presents a new despeckling method based on Non-Subsampled Contourlet Transform (NSCT) and Bayesian Maximum A Posterior (BMAP) estimation. NSCT effectively captures the SAR image features as multi-scale and multidirectional information. BMAP is a point estimation based on statistical prior distribution. So, BMAP estimation represents the aggregate behavior in each direction of the NSCT neighborhood coefficients using the statistical prior models. The dependency relationship of NSCT neighborhood coefficients by the statistical priors and BMAP of point estimation shrinks the speckle noise coefficients. In this work, the NSCT higher frequency coefficients are de-speckled, since higher frequency coefficients contain more detail and more noise. This despeckling method is compared with the state-of-the-art methods using a set of reference and non-referenced quality metrics. Experimental results show that this developed method is superior to the other methods used for preserving information and for eliminating speckle noise.

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“…This method assumes the prior distribution of the target and noise and estimates the target via probability and statistics. In recent years, research has mainly focused on maximum likelihood estimation (MLE) [28,29] and maximum a posteriori estimation (MAPE) [30,31]. These methods have also recently been utilized in radar imaging [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

A Sparse Denoising-Based Super-Resolution Method for Scanning Radar Imaging

Zhang

Huang

et al. 2021

Remote Sensing

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Scanning radar enables wide-range imaging through antenna scanning and is widely used for radar warning. The Rayleigh criterion indicates that narrow beams of radar are required to improve the azimuth resolution. However, a narrower beam means a larger antenna aperture. In practical applications, due to platform limitations, the antenna aperture is limited, resulting in a low azimuth resolution. The conventional sparse super-resolution method (SSM) has been proposed for improving the azimuth resolution of scanning radar imaging and achieving superior performance. This method uses the L1 norm to represent the sparse prior of the target and solves the L1 regularization problem to achieve super-resolution imaging under the regularization framework. The resolution of strong-point targets is improved efficiently. However, for some targets with typical shapes, the strong sparsity of the L1 norm treats them as strong-point targets, resulting in the loss of shape characteristics. Thus, we can only see the strong points in its processing results. However, in some applications that need to identify targets in detail, SSM can lead to false judgments. In this paper, a sparse denoising-based super-resolution method (SDBSM) is proposed to compensate for the deficiency of traditional SSM. The proposed SDBSM uses a sparse minimization scheme for denoising, which helps to reduce the influence of noise. Then, the super-resolution imaging is achieved by alternating iterative denoising and deconvolution. As the proposed SDBSM uses the L1 norm for denoising rather than deconvolution, the strong sparsity constraint of the L1 norm is reduced. Therefore, it can effectively preserve the shape of the target while improving the azimuth resolution. The performance of the proposed SDBSM was demonstrated via simulation and real data processing results.

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An algorithm for multi-sensor image fusion using maximum a posteriori and nonsubsampled contourlet transform

Cited by 33 publications

References 18 publications

Remote Sensing Image Fusion Based on Sparse Representation and Guided Filtering

Remote Sensing Image Fusion Based on Sparse Representation and Guided Filtering

An Enhanced Approach to Despeckle SAR Images

A Sparse Denoising-Based Super-Resolution Method for Scanning Radar Imaging

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