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Conference on image processing - Algorithms and Systems V

作者： Wharton, Eric Panetta, Karen Agaian, Sos Tufts Univ 161 Coll Ave Medford MA 02155 USA Univ Texas San Antonio San Antonio TX 78249 USA

ISBN: (纸本)9780819466105

image enhancement is the task of applying certain alterations to an input image such as to obtain a more visually pleasing image. The alteration usually requires interpretation and feedback from a human evaluator of the output resulting image. Therefore, image enhancement is considered a difficult task when attempting to automate the analysis process and eliminate the human intervention. Furthermore, images that do not have uniform brightness pose a challenging problem for image enhancement systems. Different kinds of histogram equalization techniques have been employed for enhancing images that have overall improper illumination or are over/under exposed. However, these techniques perform poorly for images that contain various regions of improper illumination or improper exposure. In this paper, we introduce new human vision model based automatic image enhancement techniques, multi-histogram equalization as well as local and adaptive algorithms. These enhancement algorithms address the previously mentioned shortcomings. We present a comparison of our results against many current local and adaptive histogram equalization methods. Computer simulations are presented shoving that the proposed algorithms outperform the other algorithms in two important areas. First, they have better performance, both in terms of subjective and objective evaluations, then that currently used algorithms on a series of poorly illuminated images as well as images with uniform and non-uniform illumination, and images with improper exposure. Second, they better adapt to local features in an image, in comparison to histogram equalization methods which treat the images globally.

关键词： histogram equalization human visual system image enhancement performance measure logarithmic image processing adaptive multi-histogram equalization

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logarithmic Morphological Neural Nets Robust to Lighting Variations 2nd

Logarithmic Morphological Neural Nets Robust to Lighting Var...

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2nd International Conference on Discrete Geometry and Mathematical Morphology (DGMM)

作者： Noyel, Guillaume Barbier-Renard, Emile Jourlin, Michel Fournel, Thierry Univ Jean Monnet Lab Hubert Curien UMR CNRS 5516 F-42000 St Etienne France

ISBN: (纸本)9783031198960;9783031198977

Morphological neural networks allow to learn the weights of a structuring function knowing the desired output image. However, those networks are not intrinsically robust to lighting variations in images with an optical cause, such as a change of light intensity. In this paper, we introduce a morphological neural network which possesses such a robustness to lighting variations. It is based on the recent framework of logarithmic Mathematical Morphology (LMM), i.e. Mathematical Morphology defined with the logarithmic image processing (LIP) model. This model has a LIP additive law which simulates in images a variation of the light intensity. We especially learn the structuring function of a LMM operator robust to those variations, namely: the map of LIP-additive Asplund distances. Results in images show that our neural network verifies the required property.

关键词： Morphological neural nets logarithmic image processing logarithmic Mathematical Morphology Robustness to lighting variations Functional Asplund metric

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logarithmic MULTIRESOLUTION ANALYSIS

LOGARITHMIC MULTIRESOLUTION ANALYSIS

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IEEE International Conference on image processing (ICIP)

作者： Navarro, Laurent Jourlin, Michel Courbebaisse, Guy Ecole Natl Super Mines CIS EMSE CNRS UMR 5307LGF F-42023 St Etienne France Univ St Etienne Lab H Curien CNRS UMR 5516 St Etienne France UCB Lyon1 CREATIS CNRS UMR 5220INSERMU1044INSA Lyon Lyon France

ISBN: (纸本)9781479983391

Recently, a logarithmic image processing model called Symmetric logarithmic image processing (S-LIP) has been investigated in the framework of the multiresolution analysis (MRA) performed by wavelet transform. The S-LIP model is an extension of the logarithmic image processing (LIP) model. The motivation of this work is to implement classical wavelet applications in the S-LIP framework. The underlying idea is to take advantage of both the multiscale analysis performed by the wavelet transform and the logarithmic processing of the pixels' intensity by the S-LIP model. The S-LIP wavelet transform is introduced and applied to automatic denoising in order to highlight its intrinsic characteristics. As an illustration, signal-to-Noise Ratios for both the linear wavelet transform and S-LIP wavelet transform are calculated for different levels of Gaussian, Poisson, Speckle and salt-and-pepper noises.

关键词： Wavelet Transform logarithmic image processing Symmetric logarithmic image processing Denoising

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A Link Between the Multiplicative and Additive Functional Asplund's Metrics 14th

A Link Between the Multiplicative and Additive Functional As...

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14th International Symposium on Mathematical Morphology (ISMM)

作者： Noyel, Guillaume Univ Strathclyde Inst Global Publ Hlth Lyon France Int Prevent Res Inst Lyon France

ISBN: (纸本)9783030208677;9783030208660

Functional Asplund's metrics were recently introduced to perform pattern matching robust to lighting changes thanks to double-sided 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 LIP-multiplicative distance map. Experiments will illustrate this relation and the robustness of the LIP-additive Asplund's metric to lighting changes.

关键词： Pattern Recognition Insensitivity to lighting changes logarithmic image processing Mathematical Morphology Asplund's metric Double-sided probing Relation between functional Asplund's metrics

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Face Recognition Based on logarithmic Local Binary Patterns

Face Recognition Based on Logarithmic Local Binary Patterns

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Conference on image processing - Algorithms and Systems XI

作者： Mandal, Debashree Panetta, Karen Agaian, Sos Tufts Univ Dept Elect & Comp Engn Medford MA 02155 USA

ISBN: (纸本)9780819494283

This paper presents a novel approach to the problem of face recognition that combines the classical Local Binary Pattern (LBP) feature descriptors with image processing in the logarithmic domain and the human visual system. Particularly, we have introduced parameterized logarithmic image processing (PLIP) operators based LBP feature extractor. We also use the human visual system based image decomposition, which is based on the Weber's law to extract features from the decomposed images and combine those with the features extracted from the original images thereby enriching the feature vector set and obtaining improved rates of recognition. Comparisons with other methods are also presented. Extensive experiments clearly show the superiority of the proposed scheme over LBP feature descriptors. Recognition rates as high as 99% can be achieved as compared to the recognition rate of 96.5% achieved by the classical LBP using the AT&T Laboratories face database.

关键词： Local Binary Patterns logarithmic image processing Feature Extraction Human Visual System

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Measurements of Rainfall Rates From Videos 10

Measurements of Rainfall Rates From Videos

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10th International Congress on image and Signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Dong, Rong Liao, Juan Li, Bo Zhou, Huiyu Crookes, Danny Nantong Univ Sch Elect & Informat Nantong Peoples R China Nantong Univ Sch Elect Sci & Engn Nantong Peoples R China Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast Antrim North Ireland

ISBN: (纸本)9781538619377

Measuring rainfall rates from videos is a novel research topic. Due to rain motion, reflection of light and background clutter, it is extremely challenging to obtain accurate measurements. In this paper, we propose a new technique for measuring rainfall rates from videos, which consists of the following technical steps: first, we detect raindrops in an image using gray-tone functions and direction of rain streaks;we then select the focused raindrops, based on two features: average color tensor response and average intensity difference. Afterwards, the size of the raindrops is estimated and a raindrop size distribution (RSD) curve is created according to the use of the RSD in meteorology. Finally, a rainfall rate is obtained by fitting the RSD curve with a Gamma distribution model. In the experiment section presented in this paper, the proposed algorithm is evaluated under different light, moderate and heavy rainy conditions. The measurement results of the proposed algorithm are consistent with those of a can-type rain gauge.

关键词： color tensor logarithmic image processing rain detection rainfall rate raindrop size distribution

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Extending Camera's Capabilities in Low Light Conditions Based on LIP Enhancement Coupled with CNN Denoising

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SENSORS 2021年第23期21卷 7906-7906页

作者： Carre, Maxime Jourlin, Michel NT2I Co F-42000 St Etienne France Jean Monnet Univ Hubert Curien Lab F-42000 St Etienne France

Using a sensor in variable lighting conditions, especially very low-light conditions, requires the application of image enhancement followed by denoising to retrieve correct information. The limits of such a process are explored in the present paper, with the objective of preserving the quality of enhanced images. The LIP (logarithmic image processing) framework was initially created to process images acquired in transmission. The compatibility of this framework with the human visual system makes possible its application to images acquired in reflection. Previous works have established the ability of the LIP laws to perform a precise simulation of exposure time variation. Such a simulation permits the enhancement of low-light images, but a denoising step is required, realized by using a CNN (Convolutional Neural Network). A main contribution of the paper consists of using rigorous tools (metrics) to estimate the enhancement reliability in terms of noise reduction, visual image quality, and color preservation. Thanks to these tools, it has been established that the standard exposure time can be significantly reduced, which considerably enlarges the use of a given sensor. Moreover, the contribution of the LIP enhancement and denoising step are evaluated separately.

关键词： low-light image enhancement logarithmic image processing denoising CNN image quality metrics

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logarithmic MULTIRESOLUTION ANALYSIS

LOGARITHMIC MULTIRESOLUTION ANALYSIS

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IEEE International Conference on image processing

作者： Laurent Navarro Michel Jourlin Guy Courbebaisse Ecole Nationale Superieure des Mines CIS-EMSE CNRS: UMR 5307 University of Saint-Etienne Lab. H. Curien UMR CNRS 5516 University of Lyon CREATIS CNRS UMR 5220 INSERM U1044 UCB Lyon1 INSA Lyon

ISBN: (纸本)9781479983407

关键词： Wavelet Transform logarithmic image processing Symmetric logarithmic image processing Denoising

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DIGITAL ELECTRONIC ARITHMETICS WITH APPLICATIONS

DIGITAL ELECTRONIC ARITHMETICS WITH APPLICATIONS

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2007 IEEE International Conference on System of Systems Engineering (SOSE 2007), vol.2

作者： Eric Wharton Karen Panetta Sos Agaian Department of Electrical and Computer Engineering Tufts University Medford MA USA University of Texas San Antonio San Antonio TX USA

In this paper we introduce a new parametric image processing framework to more accurately process images. This Parameterized Digital Electronic Arithmetic (PDEA) model replaces linear arithmetic with non-linear operations which more accurately model the non-linear properties of image processing. In this paper, the parameterized model is presented, including experimental methods for choosing parameters.

关键词： image Enhancement logarithmic image processing Measure of Enhancement

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