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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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Bone SPECT image reconstruction using deconvolution and wavelet transformation: Development, performance assessment and comparison in phantom and patient study with standard OSEM and resolution recovery algorithm

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PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS 2014年第7期30卷 858-864页

作者： Ptacek, Jaroslav Henzlova, Lenka Koranda, Pavel Univ Hosp Olomouc Dept Nucl Med Olomouc 77520 Czech Republic Univ Hosp Olomouc Dept Med Phys & Radiat Protect Olomouc 77520 Czech Republic

Purpose: The aim of this work was to introduce a new algorithm for image reconstruction in bone SPECT and to compare its performances with a commercially available standard OSEM and resolution recovery (RR) reconstruction. Materials and methods: The algorithm was built applying the Lucy-Richardson deconvolution adn logarithmic image processing to the projections. A modification of the coefficients of wavelet decomposition was used to suppress the noise. The comparison with vendor software was performed both in a phantom study, using Signal-to-Noise ratio (SNR), Signal-to-Background ratio (SBR), spatial resolution and in clinical studies, by visual assessment of changes in contrast, spatial resolution and lesion detectability. Results: A change in the SNR (from 4 to 40%), an increase in the SBR (from 19 to 40%), a minor improvement in spatial resolution and a similar noise level were observed in the phantom study in comparison to the standard OSEM. A decrease in the SNR, a worse spatial resolution, but only a 3 to 13% lower SBR were achieved in comparison with the vendor supplied RR algorithm. The proposed algorithm creates patient images with better contrast and lesion detectability compared to clinically used OSEM. Compared to RR, more than half of obtained images showed better contrast and nearly half of them have better lesion detectability. Conclusion: The proposed algorithm compares favorably with the standard OSEM. Although less favorable, the comparison with RR and noise suppression algorithms, suggests that it can be used with only a slight decrease in the SBR. (C) 2014 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

关键词： Deconvolution logarithmic image processing Wavelet denoising

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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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An Improved Visual Enhancement Method for Color images

An Improved Visual Enhancement Method for Color Images

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5th International Conference on Digital image processing (ICDIP)

作者： Wang, Wenhan Zhang, Biao Liaoning Tech Univ Elect & Informat Engn Coll Huludao 125000 Peoples R China Commun Univ Informat Engn Sch Beijing Peoples R China

ISBN: (纸本)9780819493057

The paper presents an improved method for color image enhancement based on dynamic range compression and shadow compensation method proposed by Felix Albu et al. The improved method consists of not only the dynamic range compression and shadow compensation but also intensity contrast stretching implemented under the logarithmic image processing (LIP) model. The previous method enhance image while preserve image details and color information without generating visual artifacts. On the premise of remaining the advantages of the previous one, our improved method enhances the intensity of the whole image especially the low-light areas. The experimental results illustrate the effectiveness of our proposed method and the superiority over the previous one.

关键词： Color image enhancement logarithmic image processing Dynamic range compression Shadow compensation Intensity contrast stretching

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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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Medical image enhancement using logarithmic image processing

Medical image enhancement using logarithmic image processing

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5th IASTED International Conference on Visualization, Imaging, and image processing

作者： Zaharescu, E Ovidius Univ Constanta Constanta 900527 Romania

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.

关键词： medical imagery image enhancement logarithmic image processing real vector space Euclidean space

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Human visual system-based image enhancement and logarithmic contrast measure

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2008年第1期38卷 174-188页

作者： Panetta, Karen A. Wharton, Eric J. Agaian, Sos S. Tufts Univ Dept Elect & Comp Engn Medford MA 02155 USA Univ Texas San Antonio Coll Engn San Antonio TX 78249 USA

Varying scene illumination poses many challenging problems for machine vision systems. One such issue is developing global enhancement methods that work effectively across the varying illumination. In this paper, we introduce two novel image enhancement algorithms: edge-preserving contrast enhancement, which is able to better preserve edge details while enhancing contrast in images with varying illumination, and a novel multihistogram equalization method which utilizes the human visual system (HVS) to segment the image, allowing a fast and efficient correction of nonuniform illumination. We then extend this HVS-based multihistogram equalization approach to create a general enhancement method that can utilize any combination of enhancement algorithms for an improved performance. Additionally, we propose new quantitative measures of image enhancement, called the logarithmic Michelson contrast measure (AME) and the logarithmic AME by entropy. Many image enhancement methods require selection of operating parameters, which are typically chosen using subjective methods, but these new measures allow for automated selection. We present experimental results for these methods and make a comparison against other leading algorithms.

关键词： enhancement measure human visual system (HVS) image enhancement logarithmic image processing

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Human visual system based image enhancement

Human visual system based image enhancement

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Conference on Mobile Multimedia/image processing for Military and Security Applications

作者： Wharton, Eric J. Panetta, Karen A. Agaian, Sos S. Tufts Univ Dept Elect & Comp Engn 161 Coll Ave Medford MA 02155 USA Univ Texas San Antonio Coll Engn San Antonio TX 78249 USA

ISBN: (纸本)9780819467010

This paper presents a method of image enhancement using an adaptive thresholding method based on the human visual system. We utilize a number of different enhancement algorithms applied selectively to the different regions of an image to achieve a better overall enhancement than applying a single technique globally. The presented method is useful for images that contain various regions of improper illumination. It is also practical for correcting shadows. This thresholding system allows various enhancement algorithms to be used on different sections of the image based on the local visual characteristics. It further allows the parameters to be tuned differently for the specific regions, giving a more visually pleasing output image. We demonstrate the algorithm and present results for several high quality images as well as lower quality images such as those captured using a cell phone camera. We then compare and contrast our method to other state-of-the-art enhancement algorithms.

关键词： human vision image enhancement performance measure logarithmic image processing

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Digital electronic arithmetics with applications

Digital electronic arithmetics with applications

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IEEE International Conference on System of Systems Engineering

作者： Wharton, Eric Panetta, Karen Agaian, Sos Tufts Univ Dept Elect & Comp Engn Medford MA 02155 USA Univ Texas San Antonio San Antonio TX 78249 USA

ISBN: (纸本)9781424411597

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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Adaptive multi-histogram equalization using human vision thresholding

Adaptive multi-histogram equalization using human vision thr...

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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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