文件名称:
linux指令全集 包含了linux的全部指令
开发工具:
文件大小: 292kb
下载次数: 0
上传时间: 2010-05-18
详细说明： Human Visual System-Based Image Enhancement and Logarithmic Contrast Measure Karen A. Panetta, Fellow, IEEE, Eric J. Wharton, Student Member, IEEE, and Sos S. Agaian, Senior Member, IEEE Abstract—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 quantitativemeasures of image enhancement, called the logarithmicMichelson 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. Index Terms—Enhancement measure, human visual system (HVS), image enhancement, logarithmic image processing. I. INTRODUCTION PROVIDING digital images with good contrast and detail is required for many important areas such as vision, remote sensing, dynamic scene analysis, autonomous navigation, and biomedical image analysis [3]. Producing visually natural images or modifying an image to better show the visual information contained within the image is a requirement for nearly all vision and image processing methods [9]. Methods for obtaining such images from lower quality images are called image enhancement techniques. Much effort has been spent in extracting information from properly enhanced images [1], [2], [4]–[8]. The enhancement task, however, is complicated by the lack of any general unifying theory of image enhancement as well as the lack of an effective quantitative standard of image Manuscript received February 26, 2007; revised June 8, 2007. This work was supported in part by the National Science Foundation under Award 0306464. This paper was recommended by Associate Editor P. Bhattacharya. K. A. Panetta and E. J. Wharton are with the Department of Electrical and Computer Engineering, Tufts University, Medford, MA 02155 USA (e-mail: karen@eecs.tufts.edu; ewhart02@eecs.tufts.edu). S. S. Agaian is with the College of Engineering, University of Texas at San Antonio, San Antonio, TX 78249 USA, and also with the Department of Electrical and Computer Engineering, Tufts University, Medford, MA 02155 USA (e-mail: sos.agaian@utsa.edu). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TSMCB.2007.909440 quality to act as a design criterion for an image enhancement system. Furthermore, many enhancement algorithms have external parameters which are sometimes difficult to fine-tune [11]. Most of these techniques are globally dependent on the type of input and treat images instead of adapting to local features within different regions [12]. A successful automatic image enhancement requires an objective criterion for enhancement and an external evaluation of quality [9]. Recently, several models of the human visual system (HVS) have been used for image enhancement. One method is to attempt to model the transfer functions of the parts of the HVS, such as the optical nerve, cortex, and so forth. This method then attempts to implement filters which recreate these processes to model human vision [41], [42]. Another method uses a single channel to model the entire system, processing the image with a global algorithm [42]. HVS-based image enhancement aims to emulate the way in which the HVS discriminates between useful and useless data [34]. Weber’s Contrast Law quantifies the minimum change required for the HVS to perceive contrast; however, this only holds for a properly illuminated area. The minimum change required is a function of background illumination and can be closely approximated with three regions. The first is the Devries–Rose region, which approximates this threshold for under-illuminated areas. The second and most well known region is the Weber region, which models this threshold for properly illuminated areas. Finally, there is the saturation region, which approximates the threshold for over-illuminated areas [33]. Each of these regions can be separately enhanced and recombined to form a more visually pleasing output image. In this paper, we propose a solution to these image enhancement problems. The HVS system of image enhancement first utilizes a method which segments the image into three regions with similar qualities, allowing enhancement methods to be adapted to the local features. This segmentation is based upon models of the HVS. The HVS system uses an objective evaluation measure for selection of parameters. This allows for more consistent results while reducing the time required for the enhancement process. The performance measure utilizes established methods of measuring contrast and processes these values to assess the useful information contained in the image. Operating parameters are selected by performing the enhancement with all practical values of the parameters, by assessing each output image using the measure, and by organizing these results into a graph of performance measure versus parameters, where the best parameters are located at local extrema. 1083-4419/$25.00 © 2007 IEEE PANETTA et al.: HUMAN VISUAL SYSTEM-BASED IMAGE ENHANCEMENT 175 This paper is organized as follows. Section II presents the necessary background information, including the parameterized logarithmic image processing (PLIP) model operator primitives, used to achieve a better image enhancement, several enhancement algorithms, including modified alpha rooting and logarithmic enhancement, and the multiscale center-surround Retinex algorithm, which we use for comparison purposes. Section III presents new contrast measures, such as the logarithmic Michelson contrast measure (AME) and logarithmic AME by entropy (AMEE), and a comparison with other measures used in practice, such as the measure of image enhancement (EME) and AME. Section IV introduces the new HVS-based segmentation algorithms, including HVS-based multihistogram equalization and HVS-based image enhancement. Section V discusses in detail several new enhancement algorithms, including edge-preserving contrast enhancement (EPCE) and the logarithm and AME-based weighted passband (LAW) algorithm. Section VI presents a computational analysis comparing the HVS algorithm to several state-of-the-art image enhancement algorithms, such as low curvature image simplifier (LCIS). Section VII presents the results of computer simulations and analysis, comparing the HVS algorithm to Retinex and other algorithms, showing that the HVS algorithm outperforms the other algorithms. Section VIII discusses results and provides some concluding comments. ...展开收缩
(系统自动生成,下载前可以参看下载内容)
下载文件列表
相关说明
- 本站资源为会员上传分享交流与学习,如有侵犯您的权益,请联系我们删除.
- 本站是交换下载平台，提供交流渠道，下载内容来自于网络，除下载问题外，其它问题请自行百度。
- 本站已设置防盗链，请勿用迅雷、QQ旋风等多线程下载软件下载资源，下载后用WinRAR最新版进行解压.
- 如果您发现内容无法下载，请稍后再次尝试；或者到消费记录里找到下载记录反馈给我们.
- 下载后发现下载的内容跟说明不相乎，请到消费记录里找到下载记录反馈给我们，经确认后退回积分.
- 如下载前有疑问，可以通过点击"提供者"的名字，查看对方的联系方式，联系对方咨询.
