Enhanced iterative processing algorithms for restoration and superresolution of tactical sensor imagery

Sundareshan, Malur K.

doi:10.1117/1.1626665

Cited by 5 publications

(5 citation statements)

References 27 publications

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“…Image super-resolution reconstruction algorithm is to use a series of low-resolution images to generate a high-resolution image in order to gain more information and details from the images, which has been used for SAR image resolution enhancement [6,11,21,22]. The greatest advantage of using image super-resolution reconstruction algorithm in SAR resolution enhancement is it may improve the resolution of SAR images without increasing signal spectral and lengthening synthetic aperture.…”

Section: Introductionmentioning

confidence: 98%

“…Otherwise, resolution enhancement would put forward strict requirements on the radar system and bring about great difficulties to motion compensation and imaging processing. Although, references [6,11,21,22] have researched on super-resolution reconstruction algorithms in the fields of SAR image processing, none of them investigated the effect of super-resolution reconstruction algorithm on SAR target recognition. We don't know whether high-resolution image generated by super-resolution reconstruction algorithm is beneficial for the advancement of SAR target recognition rate, although we have known high resolution leads to high recognition rate [12,13].…”

Section: Introductionmentioning

confidence: 98%

“…In this paper, we focus on the effect of super-resolution reconstruction algorithm on the improvement of SAR target recognition rate in the data level of our structure. We consider projections onto convex sets (POCS) algorithm [17,19,20,26] as it is a classic spatial domain super-resolution image reconstruction algorithm, which has been widely used for reconstructing and processing different kinds of images [5,7,8,22,25]. POCS algorithm is considered to reconstruct a super-resolution image from multi-aspect SAR images of a target in this paper, which may improve the resolution of SAR images and support SAR image processing and target recognition, resulting in high recognition rates.…”

Section: Introductionmentioning

confidence: 99%

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New structure for multi-aspect SAR image target recognition with multi-level joint consideration

Huan

Wang

Pan

et al. 2015

Multimed Tools Appl

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A new structure for SAR target recognition using multi-aspect SAR images is proposed. The new structure applies new approaches in the two levels of SAR image target recognition, which are data level and decision level, and combines the benefits of the two. Projections onto convex sets (POCS) super-resolution reconstruction algorithm is used in the data level of the new structure, which is to advance the resolution of the SAR image by using a series of multi-aspect SAR images. Weighted Bayes decision fusion algorithm is proposed in the decision level to jointly consider the benefit from the data level. All outcomes from the classifiers are fused in the decision level to generate the final result, which combines the multilevel benefits. Verification and analysis is performed to the proposed structure with multitarget image data in MSTAR database. Experimental results indicate that using the proposed structure for multi-aspect SAR images with multi-level joint consideration, the recognition rate is significantly advanced than that using single SAR target image. Meanwhile, the recognition rate by this structure is also higher than that using individual level approach.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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New structure for multi-aspect SAR image target recognition with multi-level joint consideration

Huan

Wang

Pan

et al. 2015

Multimed Tools Appl

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“…The portions of the scenes lost through the antenna are the high-frequency components that accurately describe the objects in the scene and are crucial for the resolution of the radar image. There are many deconvolution algorithms to improve the angular resolution of real beam beyond the classical limit by incorporating the statistical characteristics of prior information about scenes [11,12].…”

Section: Introductionmentioning

confidence: 99%

An Iterative Shrinkage Deconvolution for Angular Superresolution Imaging in Forward-Looking Scanning Radar

Zha¹,

Huang²,

Yang³

2016

PIER B

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The aim of angular super-resolution is to surpass the real-beam resolution. In this paper, a method for forward-looking scanning radar angular super-resolution imaging through a deconvolution method is proposed, which incorporates the prior information of the target's scattering characteristics. We first mathematically formulate the angular super-resolution problem of forward-looking scanning radar as a maximum a posteriori (MAP) estimation task based on the forward model, and convert it to an equivalent unconstrained optimization problem by applying the log-transforms to the posterior probability, which guarantees the solution converges to a global optimum of an associated MAP problem and it is easy to implement. We then implement the unconstrained optimization task in convex optimization framework using an iterative shrinkage method, and the computational complexity of the proposed algorithm is also discussed. Since the anti log-likelihood of the noise distribution and the prior knowledge of the scene are utilized, the proposed method is able to achieve angular super-resolution imaging in forward-looking scanning radar effectively. Numerical simulations and experimental results based on real data are presented to verify that the proposed deconvolution algorithm has better performance in preserving angular super-resolution accuracy and suppressing the noise amplification.

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“…The data recorded at the output of the radar system are a low-pass-filtered version of the original scene due to the finite size of the antenna. The portions lost by the antenna are the high frequency spectral components [ 13 ]. Convolution in the Fourier domains corresponds to multiplication, while deconvolution is Fourier division [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Deconvolution for Angular Super-Resolution in Forward-Looking Scanning Radar

Zha

Huang

Sun

et al. 2015

Sensors

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Scanning radar is of notable importance for ground surveillance, terrain mapping and disaster rescue. However, the angular resolution of a scanning radar image is poor compared to the achievable range resolution. This paper presents a deconvolution algorithm for angular super-resolution in scanning radar based on Bayesian theory, which states that the angular super-resolution can be realized by solving the corresponding deconvolution problem with the maximum a posteriori (MAP) criterion. The algorithm considers that the noise is composed of two mutually independent parts, i.e., a Gaussian signal-independent component and a Poisson signal-dependent component. In addition, the Laplace distribution is used to represent the prior information about the targets under the assumption that the radar image of interest can be represented by the dominant scatters in the scene. Experimental results demonstrate that the proposed deconvolution algorithm has higher precision for angular super-resolution compared with the conventional algorithms, such as the Tikhonov regularization algorithm, the Wiener filter and the Richardson–Lucy algorithm.

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Enhanced iterative processing algorithms for restoration and superresolution of tactical sensor imagery

Cited by 5 publications

References 27 publications

New structure for multi-aspect SAR image target recognition with multi-level joint consideration

New structure for multi-aspect SAR image target recognition with multi-level joint consideration

An Iterative Shrinkage Deconvolution for Angular Superresolution Imaging in Forward-Looking Scanning Radar

Bayesian Deconvolution for Angular Super-Resolution in Forward-Looking Scanning Radar

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