An iterative restoration technique

Singh, Sandeep; Tandon, S. N.; Gupta, H. M.

doi:10.1016/0165-1684(86)90091-5

Cited by 25 publications

(6 citation statements)

References 19 publications

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“…of the imaging system, is the convolution operator, n is the noise, and g is the corrupted image. In general, the nonlinear iterative restoration algorithms (Meinel, 1986;Singh et al, 1986;Stewart and Durrani, 1986;;Hunt, 1994;Archer and Titterington, 1995) to enhance image quality by restoring the high-frequency spectrum of the corrupted images can be simply modeled as the following form:…”

Section: Image Restoration Algorithmsmentioning

confidence: 99%

Image restoration through regularization based on error energy minimization

Pan

2010

Int J Imaging Syst Tech

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Image restoration has been an important field of image processing for decades. Various methods of restoration have been studied. Meanwhile, regularization presents a very general methodology for image restoration. However, more regularization will remove more noise but will also lose more image detail, and vice versa. In this article, a novel image restoration algorithm is presented, which is derived from regularization by minimizing error energy of noise and ring artifact. Thus, this proposed algorithm is not only immune to noise but also ringing artifact, where a designed high-pass filter is used in the algorithm proposed here such as the difference of a delta function and a blurring function or an edge operator like a Laplacian operator. This proposed algorithm can achieve high-resolution images by being implemented with a 1D noiseless signal to reveal its ability of generating high frequencies beyond the diffraction limit and with a 2D computer-generated noisy image to show the ability of being immune to noise as well as with a 94-GHz millimeter-wave image to validate its reliability.

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Section: Image Restoration Algorithmsmentioning

confidence: 99%

Image restoration through regularization based on error energy minimization

Pan

2010

Int J Imaging Syst Tech

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“…Recently, deconvolution algorithms in Bayesian framework or sparse signal reconstruction have been used in the application of radar imaging to improve cross-range resolution [18][19][20][21][22][23][24][25][26][27], but the tradeoff between robustness and resolution performance cannot be easily adjusted. In [28][29][30], a constrained iterative deconvolution (CID) algorithm was proposed to obtain well-behaved results due to positive constraint, but the angular resolution improvement is still limited by noise when the signal-to-noise ratio (SNR) is relatively low. Most of deconvolution algorithms are performed in the case of single channel thus the performance of deconvolution isn't very well.…”

Section: Introductionmentioning

confidence: 99%

“…Relationship of angular spatial spectrum of antenna pattern and singular value distribution of observation matrix will be discussed with evaluation parameters to describe the performance of deconvolution problem. After establishing multi-channel deconvolution model in Section 4, we will present two multi-channel deconvolution algorithms, termed as multi-channel maximum a posteriori-based deconvolution (MMAP) and multi-channel constrained iterative deconvolution (MCID), which improve upon the traditional MAP [19,20] and CID [28][29][30] algorithm via combining with multi-channel deconvolution technique. In Section 5, extensive simulated and real measured data are presented to validate the effectiveness of proposed methods.…”

Section: Introductionmentioning

confidence: 99%

Multi-Channel Deconvolution for Forward-Looking Phase Array Radar Imaging

Xia

Chen

2017

Remote Sensing

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The cross-range resolution of forward-looking phase array radar (PAR) is limited by the effective antenna beamwidth since the azimuth echo is the convolution of antenna pattern and targets' backscattering coefficients. Therefore, deconvolution algorithms are proposed to improve the imaging resolution under the limited antenna beamwidth. However, as a typical inverse problem, deconvolution is essentially a highly ill-posed problem which is sensitive to noise and cannot ensure a reliable and robust estimation. In this paper, multi-channel deconvolution is proposed for improving the performance of deconvolution, which intends to considerably alleviate the ill-posed problem of single-channel deconvolution. To depict the performance improvement obtained by multi-channel more effectively, evaluation parameters are generalized to characterize the angular spectrum of antenna pattern or singular value distribution of observation matrix, which are conducted to compare different deconvolution systems. Here we present two multi-channel deconvolution algorithms which improve upon the traditional deconvolution algorithms via combining with multi-channel technique. Extensive simulations and experimental results based on real data are presented to verify the effectiveness of the proposed imaging methods.

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“…Generally, the non-linear iterative restoration algorithms (Archer & Titterington, 1995;Hunt, 1994;Meinel, 1986;Singh et al, 1986;Stewart & Durrani, 1986) to enhance image quality by restoring the high frequency spectrum of the corrupted images can be simply modelled as the following form:…”

Section: Image Restoration Algorithmsmentioning

confidence: 99%

Image Restoration for Long-Wavelength Imaging Systems

Pan¹

2012

Image Restoration - Recent Advances and Applications

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An iterative restoration technique

Cited by 25 publications

References 19 publications

Image restoration through regularization based on error energy minimization

Image restoration through regularization based on error energy minimization

Multi-Channel Deconvolution for Forward-Looking Phase Array Radar Imaging

Image Restoration for Long-Wavelength Imaging Systems

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