On the Inter-Conversion Between Hermite and Laguerre Local Image Expansions

Claudio, Elio D. Di; Jacovitti, G.; Laurenti, A.

doi:10.1109/tip.2011.2150232

Cited by 5 publications

(10 citation statements)

References 27 publications

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“…The VRF is the simplest functional spatial vision model based on HAFs. It coincides with the first order component of the orthogonal family of the Laguerre Gauss (LG) functions (Jacovitti & Neri, 2000;Victor & Knight, 2003;Massey & Refregier, 2005) or, equivalently, of the 2D Hermite functions, which span the same signal space (Martens, 1990;Di Claudio, Jacovitti, & Laurenti, 2011). Higher order LG analysis provides functional spatial vision models oriented to structures more complex that simple edges (Neri & Jacovitti, 2004;Di Claudio, Jacovitti, & Laurenti, 2010).…”

Section: Figmentioning

confidence: 92%

Predicting blur visual discomfort for natural scenes by the loss of positional information

2021

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The perception of blur due to accommodation failures, insufficient optical correction or imperfect image reproduction is a common source of visual discomfort, usually attributed to an anomalous and annoying distribution of the image spectrum in the spatial frequency domain. In the present paper, this discomfort is related to a loss of the localization accuracy of the observed patterns. It is assumed, as a starting perceptual principle, that the visual system is optimally adapted to pattern localization in a natural environment. Thus, since the best possible accuracy of the image patterns localization is indicated by the positional Fisher Information, it is argued that blur discomfort is strictly related to a loss of this information. Following this concept, a receptive field functional model is adopted to predict the visual discomfort. It is a complex-valued operator, orientationselective both in the space domain and in the spatial frequency domain. Starting from the case of Gaussian blur, the analysis is extended to a generic type of blur by applying a positional Fisher Information equivalence criterion. Out-of-focus blur and astigmatic blur are presented as significant examples. The validity of the proposed model is verified by comparing its predictions with subjective ratings. The model fits linearly with the experiments reported in independent databases, based on different protocols and settings.

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Section: Figmentioning

confidence: 92%

Predicting blur visual discomfort for natural scenes by the loss of positional information

2021

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“…Recall that orthogonal polynomials are widely applied in wavelet theory and signal processing. See, for example, [ 66 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 ].…”

Section: Two Wavelet/multiwavelet Processorsmentioning

confidence: 99%

“…Recall that orthogonal polynomials are widely applied in wavelet theory and signal processing. See, for example, [66,[71][72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87].…”

Section: Clifford Wavelets and Multiwaveletsmentioning

confidence: 99%

Clifford Wavelet Entropy for Fetal ECG Extraction

Jallouli

Arfaoui

Mabrouk

et al. 2021

Entropy

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Analysis of the fetal heart rate during pregnancy is essential for monitoring the proper development of the fetus. Current fetal heart monitoring techniques lack the accuracy in fetal heart rate monitoring and features acquisition, resulting in diagnostic medical issues. The challenge lies in the extraction of the fetal ECG from the mother ECG during pregnancy. This approach has the advantage of being a reliable and non-invasive technique. In the present paper, a wavelet/multiwavelet method is proposed to perfectly extract the fetal ECG parameters from the abdominal mother ECG. In a first step, due to the wavelet/mutiwavelet processing, a denoising procedure is applied to separate the noised parts from the denoised ones. The denoised signal is assumed to be a mixture of both the MECG and the FECG. One of the well-known measures of accuracy in information processing is the concept of entropy. In the present work, a wavelet/multiwavelet Shannon-type entropy is constructed and applied to evaluate the order/disorder of the extracted FECG signal. The experimental results apply to a recent class of Clifford wavelets constructed in Arfaoui, et al. J. Math. Imaging Vis. 2020, 62, 73–97, and Arfaoui, et al.Acta Appl. Math.2020, 170, 1–35.. Additionally, classical Haar–Faber–Schauder wavelets are applied for the purpose of comparison. Two main well-known databases have been applied, the DAISY database and the CinC Challenge 2013 database. The achieved accuracy over the test databases resulted in Se=100%, PPV=100% for FECG extraction and peak detection.

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“…The shape of the VRF is the described by a so-called harmonic angular filter (HAF), complex-valued and polarseparable function. It is the first-order component of the orthogonal family of the Laguerre Gauss (LG) functions [26,27,28] 1 .…”

Section: The Virtual Receptive Field Modelmentioning

confidence: 99%

Predicting the Blur Visual Discomfort for Natural Scenes by the Loss of Positional Information

Claudio¹,

Giannitrapani²,

Jacovitti³

2020

Preprint

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The perception of the blur due to accommodation failures, insufficient optical correction or imperfect image reproduction is a common source of visual discomfort, usually attributed to an anomalous and annoying distribution of the image spectrum in the spatial frequency domain. In the present paper, this discomfort is attributed to a loss of the localization accuracy of the observed patterns. It is assumed, as a starting perceptual principle, that the visual system is optimally adapted to pattern localization in a natural environment. Thus, since the best possible accuracy of the image patterns localization is indicated by the positional Fisher information, it is argued that the blur discomfort is highly correlated with a loss of this information. Following this concept, a receptive field functional model, tuned to common and stable features of natural scenes, is adopted to predict the visual discomfort. It is of a complex-valued operator, orientation-selective both in the space domain and in the spatial frequency domain. Starting from the case of Gaussian blur, the analysis is extended to a generic blur type by applying a positional Fisher information equivalence criterion. Out-of-focus blur and astigmatic blur are presented as significant examples. The validity of the proposed model is verified by comparing its predictions with subjective ratings of the quality loss of blurred natural images. The model fits linearly with the experiments reported in independent databases, based on different protocols and settings.

show abstract

On the Inter-Conversion Between Hermite and Laguerre Local Image Expansions

Cited by 5 publications

References 27 publications

Predicting blur visual discomfort for natural scenes by the loss of positional information

Predicting blur visual discomfort for natural scenes by the loss of positional information

Clifford Wavelet Entropy for Fetal ECG Extraction

Predicting the Blur Visual Discomfort for Natural Scenes by the Loss of Positional Information

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