The symmetric logarithmic image processing model

Navarro, Laurent; Deng, Guang; Courbebaisse, Guy

doi:10.1016/j.dsp.2013.07.001

Cited by 29 publications

(17 citation statements)

References 20 publications

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“…This last expression will be useful to establish the link with the map of LIPadditive Asplund's distances. its inverse ξ −1 were both defined in [13,18] by:…”

Section: General Expression Of the Map Of Lip-additive Asplund's Distmentioning

confidence: 99%

A Link Between the Multiplicative and Additive Functional Asplund’s Metrics

Noyel

2019

Lecture Notes in Computer Science

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Functional Asplund's metrics were recently introduced to perform pattern matching robust to lighting changes thanks to doublesided probing in the Logarithmic Image Processing (LIP) framework. Two metrics were defined, namely the LIP-multiplicative Asplund's metric which is robust to variations of object thickness (or opacity) and the LIP-additive Asplund's metric which is robust to variations of camera exposure-time (or light intensity). Maps of distances -i.e. maps of these metric values -were also computed between a reference template and an image. Recently, it was proven that the map of LIP-multiplicative Asplund's distances corresponds to mathematical morphology operations. In this paper, the link between both metrics and between their corresponding distance maps will be demonstrated. It will be shown that the map of LIP-additive Asplund's distances of an image can be computed from the map of the LIP-multiplicative Asplund's distance of a transform of this image and vice-versa. Both maps will be related by the LIP isomorphism which will allow to pass from the image space of the LIP-additive distance map to the positive real function space of the LIPmultiplicative distance map. Experiments will illustrate this relation and the robustness of the LIP-additive Asplund's metric to lighting changes.

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“…This last expression will be useful to establish the link with the map of LIPadditive Asplund's distances. its inverse ξ −1 were both defined in [13,18] by:…”

Section: General Expression Of the Map Of Lip-additive Asplund's Distmentioning

confidence: 99%

A Link Between the Multiplicative and Additive Functional Asplund’s Metrics

Noyel

2019

Lecture Notes in Computer Science

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“…What distinguishes the models is the isomorphism, since it determines the operations of the algebraic structure. There are several NIP models, but in the experimentation performed in this contribution the S-LIP model [26] was used.…”

Section: Non-linear Models For Image Processingmentioning

confidence: 99%

Multiple-level Logarithmic Wavelets for Mammographic Contrast Enhancement: A Statistical Analysis for Wavelet Selection

Santiago¹,

León²,

Mecías³

2018

CyS

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Low doses radiation in mammography results in low contrast images. In this paper we propose a method to enhance the contrast in mammography; it combines the modification of the coefficients of the Logarithmic Discrete Wavelet Transform using the Local Correlation method and Symmetric Logarithmic Image Processing model. Experimental results shown the better performance for the anomalies known as calcifications and masses. This paper also presents a methodology to select a combination of decomposition levels to be processed for good contrast improvement according to the values of measures based on region of interest. This procedure relies on Principal Components Analysis of the data. The experiments show that the chosen combination of levels can improve the contrast in mammograms, and that the regions of interest definition is an important factor to explain the poor contrast improvement of some anomalies. Keywords.Mammograms, contrast enhancement, discrete wavelet transform, symmetric logarithmic image processing model, logarithmic discrete wavelet transform. A novel technique for selecting mother wavelet function using an intelli gent fault diagnosis system.

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“…In such a case, a symmetrical LTIP model, such as the one described by Navarro et al (2013), has greater potential to lead to better results.…”

Section: 2mentioning

confidence: 99%

“…In this particular work, we are mainly interested in the logarithmic-like model introduced by Vertan et al (2008), which has a cone space structure and is named the LTIP model. A summary of existing models may be followed in the work of Navarro et al (2013). Recently, parametric extensions of LTIP models have also been introduced (Panetta et al, 2011;Florea and Florea, 2013).…”

Section: Logarithmic Type Image Processingmentioning

confidence: 99%

High Dynamic Range Imaging by Perceptual Logarithmic Exposure Merging

Florea

Vertan

Florea

2015

International Journal of Applied Mathematics and Computer Science

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In this paper we emphasize a similarity between the logarithmic type image processing (LTIP) model and the Naka-Rushton model of the human visual system (HVS). LTIP is a derivation of logarithmic image processing (LIP), which further replaces the logarithmic function with a ratio of polynomial functions. Based on this similarity, we show that it is possible to present a unifying framework for the high dynamic range (HDR) imaging problem, namely, that performing exposure merging under the LTIP model is equivalent to standard irradiance map fusion. The resulting HDR algorithm is shown to provide high quality in both subjective and objective evaluations.

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The symmetric logarithmic image processing model

Cited by 29 publications

References 20 publications

A Link Between the Multiplicative and Additive Functional Asplund’s Metrics

A Link Between the Multiplicative and Additive Functional Asplund’s Metrics

Multiple-level Logarithmic Wavelets for Mammographic Contrast Enhancement: A Statistical Analysis for Wavelet Selection

High Dynamic Range Imaging by Perceptual Logarithmic Exposure Merging

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