针对雾气环境下实际图像亮度/对比度不佳的情况，提出了整体灰度拉伸和局部对比度增强算法，改善了图像的亮度和对比度。采用基于暗原色先验的图像去雾算法来去除视频监控中常常遇到的雾霾影响。为了消除块效应，将图像分成最小的块，即对每个像素提取暗原色，并采用邻近相似性原则修正暗原色，MATLAB仿真表明，改进后的算法可以很好地去除图像中的雾气。最后，完成了基于达芬奇 DM6467的图像增强算法软件开发，实现了 4路视频的输出、切换和图像增强。增强后的图像，其 SSIM指标可提高 50%以上，该系统可以有效地去除雾气对图像的影响，满足图像去雾增强的需要。

本文针对 3D图像压缩过程产生的块效应，提出了一种基于联合字典的 3D图像去块效应方法。该方法主要包括字典训练、字典联合和去块效应三个阶段。在字典训练阶段，采用字典学习方法分别训练得到过完备的彩色字典和深度字典；在字典联合阶段，根据测试图像的稀疏特性和构造的彩色和深度字典，求出对应的彩色与深度联合字典；在去块效应阶段，通过估计重构误差阈值，并根据联合字典对图像进行去块效应处理。实验结果表明，本文提出的方法能够有效地去除编码图像的块效应，且得到较高的虚拟视点绘制质量。

No-reference quality assessment aims at designing objective assessment criteria consistent to subjective perceived quality without any knowledge about reference image. This paper proposes a no-reference quality assessment algorithm specific to JPEG images. Blocking artifact in JPEG images is caused by the block based quantization of frequency coefficients, which is equivalent to applying low pass filtering in each block. In view of this idea, the algorithm in this paper was used to realize the quality assessment of JPEG images by quantizing the difference of power spectrum distribution between inner-block and inter-block. The assessment method proposed in this paper owns low algorithm complexity, clear physical meanings, free from learning and training and other advantages. Compared with most presented algorithms, the assessment results of proposed algorithm demonstrate a higher correlation to the subjective perceived quality.