本文为美国内华达大学拉斯维加斯分校（作者：Nishikar Sapkota）的硕士论文，共62页。

本文描述了一种非线性对比度增强技术来实现数字视频中的夜视系统。它基于全局直方图均衡化算法。首先，从峰值信噪比（PSNR）和图像视觉检测两个方面对低照度环境下拍摄的图像进行全局直方图均衡化的有效性检验。我们的分析建立了一个最佳强度的存在，在夜视环境下，直方图均衡化在输出图像质量方面产生最佳结果。根据这一观察结果提出了一种直方图均衡化方法，该方法在峰值信噪比（PSNR）方面优于传统方法。该算法还应用于弱光照环境下的视频监控，以实现实时夜视。这涉及到直方图均衡化在数字视频帧中的应用，并通过计算机网络进行数据传输和缓冲。

This thesis describes a nonlinear contrast enhancement technique to implement night vision in digital video. It is based on the global histogram equalization algorithm. First, the effectiveness of global histogram equalization is examined for images taken in low illumination environments in terms of Peak signal to noise ratio (PSNR) and visual inspection of images. Our analysis establishes the existence of an optimum intensity for which histogram equalization yields the best results in terms of output image quality in the context of night vision. Based on this observation, an incremental approach to histogram equalization is developed which gives better results than the conventional approach in terms of PSNR. This algorithm is also applied to implementing video surveillance in poorly illuminated environments to achieve real time night vision. This involves the application of histogram equalization to digital video frames with data transmission and buffering over a computer network.

1.  引言

2.  项目背景

3.  具体实现与结果

4.  结论

附录A基于C#的直方图均衡

附录B基于C#的视频接收机

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