Derivation of the Lorentzian probability model for use in constrained image restoration

Alexander, Naomi E.; Lettington, Alan H.

doi:10.1080/09500340500141854

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…Second, the Huber regularization can distinguish the noise and the edge adaptively. [13] (c) SATV algorithm [16]. (d) Proposed method.…”

Section: Effect Of the Adaptive Weight Termmentioning

confidence: 99%

“…The IRF is shown in the right-bottom box (Figure 7(b)), and its width is set as 2.3. We compared the Hub-WSBD method with the TDLD [13], SATV method [16] to demonstrate its performance. The restored results are shown in Figure 7(e), 7(c), and 7(d ), respectively.…”

Section: Simulated Experimentsmentioning

confidence: 99%

“…The proposed algorithm is implemented on a computer with a 2.93GHz PC with 2GB memory. [13] using the kernel estimated by Hub-WSBD. (c) SATV algorithm [16].…”

Section: Application To Measured Datamentioning

confidence: 99%

“…Recently, some new signal processing methods, such as maximum entropy [10] sparse representation and neural networks [11], have also been applied to the PMMW image restoration problem. Lettington et al [12,13] proposed an impressive two-dimensional Lorentzian deconvolution (TDLD) method, in which the gradient histogram of the desired PMMW image is fitted with a two-dimensional Lorentzian probability density function. However, this method depends on an accurate IRF; thus, the recovered results are fragile to infra-deconvolution and over-deconvolution problem with incorrect IRFs.…”

Section: Introductionmentioning

confidence: 99%

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Parametric semi-blind deconvolution algorithm with Huber–Markov regularization for passive millimeter-wave images

Yan

Liu

Chen

et al. 2013

Journal of Modern Optics

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2013) Parametric semi-blind deconvolution algorithm with Huber-Markov regularization for passive millimeter-wave images, Journal of Modern Optics, 60:12, 970-982, Passive millimeter-wave (PMMW) images often suffer common problems of noise and blurring. A new method is proposed to estimate the instrument response function (IRF) and desired image simultaneously. The proposed variational model integrates the adaptive weight data term, image smooth term, and IRF smooth term. The major novelty of this work is that Huber-Markov regularization is adopted for PMMW image restoration, which can preserve structural details as well as suppress noise effectively. The IRF is parametrically formulated as a Gaussian-shaped function based on experimental measurements through the utilized PMMW imaging system. The alternation minimization iterative method is applied to achieve the IRF width and desired image. Comparative experimental results with some real PMMW images reveal that the proposed approach can effectively suppress noise, reduce ringing artifacts, and improve the spatial resolution.

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“…Second, the Huber regularization can distinguish the noise and the edge adaptively. [13] (c) SATV algorithm [16]. (d) Proposed method.…”

Section: Effect Of the Adaptive Weight Termmentioning

confidence: 99%

Section: Simulated Experimentsmentioning

confidence: 99%

“…The proposed algorithm is implemented on a computer with a 2.93GHz PC with 2GB memory. [13] using the kernel estimated by Hub-WSBD. (c) SATV algorithm [16].…”

Section: Application To Measured Datamentioning

confidence: 99%