CASIA OpenIR  > 毕业生  > 硕士学位论文
Alternative TitleResearch on Video and Image Quality Enhancement
Thesis Advisor刘昌平
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword视频图像增强 去雾 去抖动 Video And Image Enhancement Defogging Shaking Removal
Abstract视频图像质量增强技术对输入视频图像进行质量增强和修复,还原高质量的清晰稳定视频图像。它是机器视觉、图像工程、模式识别、人工智能等技术的基础,在视频监控、智能交通、军事和医学等领域具有广泛的应用。视频图像质量增强技术的研究具有重要理论与应用价值。 由于天气状况或视频图像采集设备自身的问题,采集的视频图像经常被雾化,难以得到图像的细节信息,导致图像的后期处理变得非常困难。本文首先给出了相关深度信息的概念,结合该信息可以有效抑制边缘光晕现象。随后本文提出了有效边缘强度以及去雾系数的计算方法,达到自适应去雾增强的目的。实验结果显示去雾增强处理后,图像的有效边缘强度具有不同程度的增强,表示图像质量的提升。图像的去雾系数也得到一定程度的下降,证明了自适应去雾增强方法的正确性和有效性。 目前去雾增强图像质量评价主要是通过主观的观测与评比。当评比的图像数据库较大时,主观评价方法难以胜任。在评价图像质量时,最主要的两个评价指标是图像的细节表现能力和色彩表现能力。本文结合有效边缘强度提出了细节还原系数的概念和计算方法。随后,本文基于CIELAB色彩空间提出了色彩强度及色彩还原系数的概念和计算方法。实验结果证明了细节还原系数和色彩还原系数与人的主观视觉判断具有很好的一致性,能够快速客观的给出去雾增强图像质量的评价。 视频图像剧烈抖动时,将严重影响视频图像的欣赏和后期处理(如运动检测等技术对视频图像的稳定性要求较高)。现有视频去抖动方法很难做到实时处理,实用性较低。大部分视频相邻帧间的抖动可以分解为水平和竖直方向的抖动,旋转和缩放变化系数较小,甚至可以忽略不计。在此前提下,本文提出了多尺度全局纹理配准方法,极大地提高了图像间配准的速度,满足实时处理的要求。本文还给出了共享边缘一致性约束的概念,对图像配准的结果进行验证,使得输出视频具有更好的连续性和一致性。
Other AbstractVideo and image enhancement technology is the one of the most important research topics of pattern recognition, image processing, and computer vision. It is broadly applied in many industry fields, such as video monitoring and controlling, intelligent traffic system, military affairs and medical treatment to enhance the stability and high quality of input video and image. Video and image enhancement is theoretically meaningful and has great importance of application. Bad weather condition and hardware defect of capturing device will result in blurry videos and images, with detail information lost. The concept of relative depth information is presented, with which the halos will be restrained effectively. And then we propose efficient edge intensity and defogging ratio to remove fog component adaptively. Experimental results demonstrate that valid edge intensity will be strengthened after defogging. And defogging ratio has correlative extent of descending, meaning the improvement of image quality. When estimating different defogging methods, evaluations are most likely based on subjective observations. Methods based on observations may fail if we have to estimate a large database of images. Detail performance and color performance are the two primary indexes. Valid edge intensity will be combined to compute the detail restoration ratio. Color intensity and color restoration ratio will be proposed base on CIELAB color space. Experimental results show that detail restoration ratio and color restoration ratio have good consistency with subjective visual judgments. Videos with serious shaking have bad visual effects and induce the failure of video processing like motion detection, which has strict demand of video stability. We notice most shaking component of neighboring frames can be decomposed to vertical and horizontal movements. And the rotation and scaling parameters are too little to ignore. Based on this finding, we propose multi-scale global texture registration method to speed up registration between frames. Additionally, we bring up the concept of shared edge consistency to rectify the registration results. Our methods can process real time and output video with good continuity and coherence.
Other Identifier200728014628035
Document Type学位论文
Recommended Citation
GB/T 7714
姚波. 视频图像质量增强技术研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2010.
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