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基于多尺度分解的红外图像细节增强算法研究
Alternative TitleResearch of Infrared Images Detail Enhancement Based on Multi-Scale Decomposition
陈宏宇1,2
Department光电信息技术研究室
Thesis Advisor惠斌
ClassificationTN215
Keyword红外图像 图像增强 双边滤波器 人眼视觉特性 小波变换
Call NumberTN215/C45/2016
Pages67页
Degree Discipline检测技术与自动化装置
Degree Name硕士
2016-05-25
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Abstract随着红外成像技术的不断发展,红外成像系统在各个领域都得到了广泛的应用。在自然场景中,红外图像通常具有很高的动态范围,而传统的显示器的显示范围十分有限,所以为了将采集到的信息能够在显示器中全部显示出来,需要对红外图像的动态范围进行压缩,同时为了不丢失目标的细节,要对其细节进行增强。基于以上原因,红外图像增强技术成为了红外成像技术领域中的重要研究课题,同时具有很高的学术意义和应用价值。本文以红外图像增强为研究对象,旨在增强图像细节信息的同时兼顾视觉效果。研究内容和成果包括:首先,对红外图像的增强算法进行了深入地分析研究,在此基础上确定基于分层思想的红外图像增强方法作为研究方向。其次,结合分层思想提出一种基于人眼视觉的红外图像细的增强方法。该方法首先利用改进的双边滤波器将图像分成基础层和细节层。基础层的处理是运用人眼视觉特性的方法将其映射到显示器可显示范围内,结合红外图像特点改进了灰度合并的方法。细节层的处理是应用了自适应增益的方法对细节层进行增强。最后将两层的处理结果进行合并量化到8bit范围内。针对基于分层思想在对比度的提升上的局限,提出了一种能够抑制背景杂波的红外图像增强算法。该方法利用人眼视觉特性的映射对近似图像进行映射,提高图像的对比度;同时根据不同尺度中包含着不同的纹理细节信息的原理,有选择地对图像各层次进行抑制和增强,有效地对杂波进行抑制,降低背景干扰。最后从主客观两个方面对本文算法进行了评价,用大量的红外图像对算法进行了测试,实验结果表明本算法能够改善红外图像对比度低和边缘模糊的缺点。编写了图像优化对比程序,方便验证不同增强算法的处理结果。
Other AbstractWith the development of infrared imaging technology,infrared imaging system is widely used in every field. In a natural scene, infrared image usually has a high dynamic range, almost 12 to14 bit. While the dynamic range of the displaying device is relatively very limited, only 8bit wide. In order to display all the detail in the display device, the dynamic range need to be compressed. In order to keep the detail, the detail needs to be enhanced. Based on the reasons above, the infrared image enhancement technology has become an important research topic in the field of infrared imaging and has an important academic significance and application value. In this paper, we studied the infrared images enhancement, whose purpose is to enhance image details and its visual effects. The main contents of this article, including: Firstly, the infrared image gray enhancement methods are analyzed, summed and classified. Based on these, we determine the layering idea methods as our research direction. Secondly, we purpose a new method that combines the layering idea with the human visual properties to enhance the infrared image. The proposed method divides the infrared image into base layer and detail layer using bilateral filter. In the processing of base layer, we use the method of human visual properties to map the dynamic range into traditional display range. In the processing of detail layer, Adaptive Gain Control (AGC) method is used to enhance the edges adaptively and reduce the noise. Finally, the two parts are recombined and quantized to 8-bit domain. Then, because of the limit of the improvement of contrast, we proposed a new infrared image enhancement method that can suppresses the background clutter. Then according to different texture details in the different scale, we choose to enhance or suppress it. This can suppress the background clutter effectively. After all these operations, the metric used to quantitatively evaluate the enhancement effect of different methods is given by the root-mean-square contrast (RMSC) and entropy. Experimental results show that this algorithm exceeds most current image enhancement methods in solving the problems of low contrast and blurry detail. Programme has been written to test the experimental results conveniently.
Language中文
Contribution Rank1
Document Type学位论文
Identifierhttp://ir.sia.cn/handle/173321/19623
Collection光电信息技术研究室
Affiliation1.中国科学院沈阳自动化研究所
2.中国科学院大学
Recommended Citation
GB/T 7714
陈宏宇. 基于多尺度分解的红外图像细节增强算法研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2016.
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