Adaptive Vector Directional Filters to Process Multichannel Images

Ponomaryov, Volodymyr; Rosales, Alberto; Gallegos, Francisco; Loboda, Igor

doi:10.1093/ietcom/e90-b.2.429

Cited by 23 publications

(24 citation statements)

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“…In the simulations, we considered the conventional triangular SAR range ambiguity function (AF) Ψ r (x 2 ) and two approximations of the SAR azimuth AF: (i) "sinc" approximation, Ψ (1) a (x 1 ) = |sinc(x 1 /a)|, and (ii) Gaussian "bell" approximation, Ψ (2) a (x 1 ) = exp (− (x 1 ) 2 /a 2 ), with the adjustable fractional parameters a [15]. Note that in the imaging radar theory [3,8], the AF is referred to as the continuous-form approximation of the ambiguity operator matrix Ψ = S + S and serves as an equivalent to the point spread function in the conventional image processing terminology [16,19]. In this paper, we present the simulations performed with two characteristic scenes.…”

Section: Simulations and Discussionmentioning

confidence: 99%

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Enhanced Radar Imaging in Uncertain Environment: A Descriptive Experiment Design Regularization Approach

Shkvarko¹,

Pérez-Meana²,

Atoche

2008

International Journal of Navigation and Observation

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A new robust technique for high-resolution reconstructive imaging is developed as required for enhanced remote sensing (RS) with imaging array radar or/and synthetic aperture radar (SAR) operating in an uncertain RS environment. The operational scenario uncertainties are associated with the unknown statistics of perturbations of the signal formation operator (SFO) in turbulent medium, imperfect array calibration, finite dimensionality of measurements, uncontrolled antenna vibrations, and random carrier trajectory deviations in the case of SAR. We propose new descriptive experiment design regularization (DEDR) approach to treat the uncertain radar image enhancement/reconstruction problems. The proposed DEDR incorporates into the minimum risk (MR) nonparametric estimation strategy the experiment design-motivated operational constraints algorithmically coupled with the worst-case statistical performance (WCSP) optimization-based regularization. The MR objective functional is constrained by the WCSP information, and the robust DEDR image reconstruction operator applicable to the scenarios with the low-rank uncertain estimated data correlation matrices is found. We report and discuss some simulation results related to enhancement of the uncertain SAR imagery indicative of the significantly increased performance efficiency gained with the developed approach.

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Section: Simulations and Discussionmentioning

confidence: 99%

“…, instead of the MSF, i.e., the adjoint operator F (1) = S + ) was accomplished. In analogy to image reconstruction quality metrics [16,19], we adopt here the quality metric defined as an improvement in the output signal-to-noise ratio (IOSNR):…”

Section: Simulations and Discussionmentioning

confidence: 99%

Enhanced Radar Imaging in Uncertain Environment: A Descriptive Experiment Design Regularization Approach

Shkvarko¹,

Pérez-Meana²,

Atoche

2008

International Journal of Navigation and Observation

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“…where b r, r'; t represents the kernel PSF in evolution time t corresponding to the continuous-form dynamic generalization of the PSF matrix D[i] specified by (13), and M defines the metrics inducing operator. …”

Section: Dedr-va-optimal Dynamic Ssp Reconstructionmentioning

confidence: 99%

Resolution-enhanced radar/SAR imaging: an experiment design framework combined with neural network-adapted variational analysis regularization

Shkvarko

Santos

Tuxpan

2011

EURASIP J. Adv. Signal Process.

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The convex optimization-based descriptive experiment design regularization (DEDR) method is aggregated with the neural network (NN)-adapted variational analysis (VA) approach for adaptive high-resolution sensing into a unified DEDR -VA-NN framework that puts in a single optimization frame high-resolution radar/SAR image formation in uncertain operational scenarios, adaptive despeckling and dynamic scene image enhancement for a variety of sensing modes. The DEDR -VA-NN method outperforms the existing adaptive radar imaging techniques both in resolution and convergence rate. The simulation examples are incorporated to illustrate the efficiency of the proposed DEDR-VA-related imaging techniques.

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“…The FD filter has been evaluated, and its performance has been compared with INR (Schulte, et al, 2007), MMKNN (Ponomaryov, et al, 2007), AMNF, WMKNN, and ABSTMKNN (Ponomaryov, et al, 2005b), VMF_SAR (Smolka, et al, 2003), WVDF1 (Lukac, 2003) and AVMF (Lukac, et al, 2004) filters. The 320x320 colour images Lena and Mandrill, were corrupted by impulsive noise in wide range of intensity, from 5% to 30%.…”

Section: Noise Suppression In Colour Images Using the Fd Filtermentioning

confidence: 99%