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Adaptive wavelet-based contrast enhancement of IR imagery

机译:基于自适应小波的红外图像对比度增强

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Abstract: This paper presents a suite of techniques called the Adaptive Wavelet-based Contrast Enhancement Method (AWCEM) for improving the subjective quality of an image for observation by a human. The central idea in these techniques is the space-varying stretching of the contrast of an image by enhancing or attenuating its detail wavelet transform coefficients. The degree of stretch is governed by a multi- resolution region of interest mask which is generated by a low-level feature extraction mechanism. Intelligent image enhancement seeks to preserve and amplify target details while at the same time suppressing the background clutter. This calls for an enhancement mechanism that (1) is adaptive across the image, (2) performs some form of low-level feature extraction and (3) uses these features to control the level of contrast stretching. The proposed algorithms use a combination of the multiscale energies, edge strengths, texture and motion as the features. Local scale- space anomalies in the image seed the regions of interest. We present the suite of techniques within an interactive environment, the AWCEM Tool, which allows the user to choose and optimize the algorithm for the particular type of data encountered in the final application. The algorithm applies well to both single-channel IR and visual imagery.!10
机译:摘要:本文提出了一套称为自适应小波的对比度增强方法(AWCEM),用于改善图像的主观质量以供人类观察。这些技术的中心思想是通过增强或减弱图像的细节小波变换系数来对图像的对比度进行时空扩展。拉伸程度由低分辨率特征提取机制生成的多分辨率感兴趣区域蒙版控制。智能图像增强功能旨在保留和放大目标细节,同时抑制背景混乱。这需要一种增强机制,即(1)在整个图像上都是自适应的;(2)执行某种形式的低级特征提取;(3)使用这些特征来控制对比度拉伸的级别。所提出的算法使用多尺度能量,边缘强度,纹理和运动的组合作为特征。图像中的局部比例尺空间异常播种了感兴趣的区域。我们在交互式环境AWCEM工具中展示了这套技术,该工具使用户可以针对最终应用中遇到的特定数据类型选择和优化算法。该算法适用于单通道红外图像和视觉图像。!10

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