The logarithmic image processing (LIP) model is a mathematical framework based on abstract linear mathematics which provides a set of specific algebraic and functional operations that can be applied to the processing ...
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The logarithmic image processing (LIP) model is a mathematical framework based on abstract linear mathematics which provides a set of specific algebraic and functional operations that can be applied to the processing of intensity images valued in a bounded range. The LIP model has been proved to be physically justified in the setting of transmitted light and to be consistent with several laws and characteristics of the human visual system. Successful application examples have also been reported in several imageprocessing areas, e.g., image enhancement, image restoration, three-dimensional image reconstruction, edge detection and image segmentation. The aim of this article is to show that the LIP model is a tractable mathematical framework for imageprocessing which is consistent with several laws and characteristics of human brightness perception. This is a survey article in the sense that it presents (almost) previously published results in a revised, refined and self-contained form. First, an introduction to the LIP model is exposed. Emphasis will be especially placed on the initial motivation and goal, and on the scope of the model. Then, an introductory summary of mathematical fundamentals of the LTP model is detailed. Next, the article aims at surveying the connections of the LIP model with several laws and characteristics of human brightness perception, namely the brightness scale inversion, saturation characteristic, Weber's and Fechner's laws, and the psychophysical contrast notion. Finally, it is shown that the LIP model is a powerful and tractable framework for handling the contrast notion. This is done through a survey of several LIP-model-based contrast estimators associated with special subparts (point, pair of points, boundary, region) of intensity images, that are justified both from a physical and mathematical point of view.
作者：
Jourlin, MichelCNRS
Lab Hubert Curien UMR 5516 18 Rue Prof Benoit Lauras F-42000 St Etienne France
The present study deals with image enhancement, which is a very common problem in imageprocessing. This issue has been addressed in multiple works with different methods, most with the sole purpose of improving the p...
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The present study deals with image enhancement, which is a very common problem in imageprocessing. This issue has been addressed in multiple works with different methods, most with the sole purpose of improving the perceived quality. Our goal is to propose an approach with a strong physical justification that can model the human visual system. This is why the logarithmic image processing (LIP) framework was chosen. Within this model, initially dedicated to images acquired in transmission, it is possible to introduce the novel concept of negative grey levels, interpreted as light intensifiers. Such an approach permits the extension of the dynamic range of a low-light image to the full grey scale in "real-time", which means at camera speed. In addition, this method is easily generalizable to colour images and is reversible, i.e., bijective in the mathematical sense, and can be applied to images acquired in reflection thanks to the consistency of the LIP framework with human vision. Various application examples are presented, as well as prospects for extending this work.
The logarithmic image processing (LIP) model is a mathematical framework which provides a specific set of algebraic and functional operations for the processing and analysis of intensity images valued in a bounded ran...
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The logarithmic image processing (LIP) model is a mathematical framework which provides a specific set of algebraic and functional operations for the processing and analysis of intensity images valued in a bounded range. The LIP model has been proved to be physically justified by that it is consistent with the multiplicative transmittance and reflectance image formation models, and with some important laws and characteristics of human brightness perception. This article addresses the edge detection problem using the LIP-model based differentiation. First, the LIP model is introduced, in particular, for the gray tones and gray tone functions, which represent intensity values and intensity images, respectively. Then, an extension of these LIP model notions, respectively called gray tone vectors and gray tone vector functions, is studied. Third, the LIP-model based differential operators are presented,focusing on their distinctive properties for imageprocessing. Emphasis is also placed on highlighting the main characteristics of the LIP-model based differentiation. Next, the LIP-Sobel based edge detection technique is studied and applied to edge detection, showing its robustness in locally small changes in scene illumination conditions and its performance in the presence of noise. Its theoretical and practical advantages over several well-known edge detection techniques, such as the techniques of Sobel, Canny, Johnson and Wallis, are shown through a general discussion and illustrated by simulation results on different real images. Finally, a discussion on the role of the LIP-model based differentiation in the current context of edge detection is presented.
This paper presents an effective colour enhancement framework for statistical and logarithmic image processing (LIP)-based enhancement algorithms. The proposed approach utilizes the fusion of partial, multiple compute...
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This paper presents an effective colour enhancement framework for statistical and logarithmic image processing (LIP)-based enhancement algorithms. The proposed approach utilizes the fusion of partial, multiple computed luminance channels with colour image channel statistics obtained from the input colour image for adaptive colour enhancement. The proposed scheme does not modify the image intensity channel, avoiding colour fading typically observed in colour images processed with conventional algorithms. The colour enhancement scheme compensates for the weaknesses of greyscale-based contrast enhancement and illumination normalization algorithms by focusing on preserving/restoring or enhancing colour. The proposed system avoids the conversion to complex, non-linear colour spaces such as HSI and HSV while producing similar results without manual adjustment of parameters. Additionally, an adaptive scheme for detection of images with unbalanced colour and uneven illumination is combined with the proposed system. Results show that the proposed scheme augments colour results of greyscale-based contrast enhancement algorithms and is relatively less complex compared to most algorithms in the literature. (C) 2017 Elsevier GmbH. All rights reserved.
作者：
Pinoli, JCPechiney
Centre de Recherches Parc Économique Centr'Alp BP 27 F38340 Voreppe France Laboratoire Image
Signal et Acoustique CNRS EP 92 École Supérieure de Chimie Physique et Electronique Bât. 308 43 Bd. du 11 Novembre 1918 B.P. 2077 F69616 Villeurbanne Cedex France
This article presents a comparative study of the multiplicative homomorphic imageprocessing (MHIP), the log-ratio imageprocessing (LRIP) and the logarithmic image processing (LIP). These three imageprocessing appro...
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This article presents a comparative study of the multiplicative homomorphic imageprocessing (MHIP), the log-ratio imageprocessing (LRIP) and the logarithmic image processing (LIP). These three imageprocessing approaches are based on abstract linear mathematics and provide specific operations and structures that have opened up new pathways to the development of imageprocessing techniques. The MHIP approach was designed for the processing of multiplied images, the LRIP approach was introduced to overcome the out-of-range problem associated with many imageprocessing techniques, while the LIP approach was developed for the processing of images valued in a bounded intensity range. First, it is claimed that an imageprocessing framework must be physically relevant, mathematically consistent, computationally tractable and practically fruitful. It is also pointed out that the classical linear imageprocessing (CLIP) is not adapted to non-linear and/or bounded range images or imaging systems, such as transmitted light images, practical digital images or the human brightness perception system. Then, the importance and usefulness of several mathematical fields, such as abstract linear algebra and abstract analysis, for image representation and processing within such image settings are discussed. Third, the MHIP, LRIP and LIP approaches are presented, focusing on their distinctive ideas, structures and properties for image representation and processing, rather than an in-depth review. Next, a study of the relationships and differences between their image representations and basic algebraic operations is detailed. Finally, a general comparative discussion is developed, showing the main physical, mathematical, computational and practical characteristics of these three abstract-linear-mathematics-based imageprocessing approaches, and summarizing their respective advantages and disadvantages. It is concluded and highlighted through real application examples in both image enha
images of a scene observed under a variable illumination or with a variable optical aperture are not identical. Does a privileged representant exist? In which physical setting? In which mathematical context? With whic...
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images of a scene observed under a variable illumination or with a variable optical aperture are not identical. Does a privileged representant exist? In which physical setting? In which mathematical context? With which meaning and criterion? How to obtain it? The authors answer to such questions in the physical setting of logarithmic imaging processes, For such a purpose, they use the logarithmic image processing (LIP) model, known to be a compatible mathematical framework. After short recalls on this model, the paper presents two image transforms: one performs an optimal enhancement and stabilization of the overall dynamic range, and the other does of the mean dynamic range. The results obtained on X-ray images, as well as for some natural scenes, are shown. Also the implementation of the transforms is addressed.
We establish the link between Mathematical Morphology and the map of Asplund's distances between a probe and a grey scale function, using the logarithmic image processing scalar multiplication. We demonstrate that...
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ISBN:
(纸本)9783319572406;9783319572390
We establish the link between Mathematical Morphology and the map of Asplund's distances between a probe and a grey scale function, using the logarithmic image processing scalar multiplication. We demonstrate that the map is the logarithm of the ratio between a dilation and an erosion of the function by a structuring function: the probe. The dilations and erosions are mappings from the lattice of the images into the lattice of the positive functions. Using a flat structuring element, the expression of the map of Asplund's distances can be simplified with a dilation and an erosion of the image;these mappings stays in the lattice of the images. We illustrate our approach by an example of pattern matching with a non-flat structuring function.
Asplund's metric, which is useful for pattern matching, consists in a double-sided probing, i.e. the over-graph and the sub-graph of a function are probed jointly. It has previously been defined for grey-scale ima...
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ISBN:
(纸本)9781479983391
Asplund's metric, which is useful for pattern matching, consists in a double-sided probing, i.e. the over-graph and the sub-graph of a function are probed jointly. It has previously been defined for grey-scale images using the logarithmic image processing (LIP) framework. LIP is a non-linear model to perform operations between images while being consistent with the human visual system. Our contribution consists in extending the Asplund's metric to colour and multivariate images using the LIP framework. Asplund's metric is insensitive to lighting variations and we propose a colour variant which is robust to noise.
The logarithmic image processing theory is a mathematical framework that provides a set of specific algebraic and functional operations and structures that are well adapted to the representation and processing of nonl...
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ISBN:
(纸本)0889865280
The logarithmic image processing theory is a mathematical framework that provides a set of specific algebraic and functional operations and structures that are well adapted to the representation and processing of nonlinear images, and more generally of non-linear signals, valued in a bounded intensity range. The purpose of this paper is to introduce a new mathematical LIP model focused on theoretical and practical aspects concerning the enhancement of the transmitted medical images and the physical absorption/transmission laws expressed within LIP mathematical framework. First of all the bounded interval (-1,1) is considered as the set of gray levels and we define two operations: addition left angle bracket+right angle bracket and real scalar multiplication left angle bracketXright angle bracket. With these operations, the set of gray levels becomes a real vector space. Then, defining the scalar product (.vertical bar.) and the norm parallel *** to, we obtain an Euclidean space of the gray levels. Secondly, we extend these operations and functions for color images. Finally, the experimental results, shown as enhanced medical images, reveal that this method has wide potential areas of impact which may include: Digital Xray, Digital Mammography, Computer Tomography Scans, Nuclear Magnetic Resonance imagery and Telemedicine Applications.
Medical image enhancement is an effective tool to improve visual quality of digital medical images. However, conventional linear image enhancement methods often suffers from problems such as over-enhancement and noise...
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ISBN:
(纸本)9781509018970
Medical image enhancement is an effective tool to improve visual quality of digital medical images. However, conventional linear image enhancement methods often suffers from problems such as over-enhancement and noise sensitivity. In this paper, we study nonlinear arithmetic frameworks designed to solve the common problems of linear enhancement methods, namely, LIP, PLIP and GLIP. We also introduce nonlinear unsharp masking algorithms based on the logarithmic image processing models for medical image enhancement. Experiments are conducted to evaluate and compare the performance of the methods.
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