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多个图像增强的源代码 包括小波增强 边缘增强 直方图滤波等等。。。(Multiple image enhancement of source code includes wavelet edge histogram filtering, enhance enhancement.)

说明：  基于最小变分方法的快速图像复原方法，图像复原质量不变的情况下，加速算法的效率。(Image restoration based on Total Variation.)

说明：  使用python语言实现多张图像的拼接，并且处理拼接之后的裂缝和黑边。(Use python language to achieve splicing of multiple images, and deal with cracks and black edges after splicing.)

初学者的游程编码，可以进行小图像的压缩，可以以此为基础进行编码和参考(RLE beginner, you can make small image compression, can be encoded as a basis and reference)

图像分割经典算法GraphCut的实现代码，亲测可用。(An implementation of GraphCut Algorithm, which is a classical method in image segmentation field.)

一个功能强大音频重采样算法，供大家学习参考(A powerful audio resampling algorithm for learning the reference U.S.)

该程序初始时使用背景差分法自动检测目标，当目标完全进入图像中后，改用meanshift方法对目标进行跟踪。(the program use background subtraction to detect the target first,when the target fully enter the screen,change to meanshift method to track the target.)

一个很好类子来说明类模板的使用，可以比较明了类模板的使用方法，最好进行单步调试(use of class template )

SIFT，即尺度不变特征变换（Scale-invariant feature transform，SIFT），是用于图像处理领域的一种描述。这种描述具有尺度不变性，可在图像中检测出关键点，是一种局部特征描述子。[1] 该方法于1999年由David Lowe[2] 首先发表于计算机视觉国际会议（International Conference on Computer Vision，ICCV），2004年再次经David Lowe整理完善后发表于International journal of computer vision（IJCV）。截止2014年8月，该论文单篇被引次数达25000余次。(SIFT, namely Scale Invariant Feature Transform (Scale-invariant feature transform, SIFT), it is a description for image processing. This description has scale invariance, can detect critical point in the image, it is a local feature descriptor. [1] The method in 1999 by David Lowe [2] First published in the International Conference on Computer Vision (International Conference on Computer Vision, ICCV), 2004 was again perfect finishing after David Lowe published in the International journal of computer vision (IJCV) . As of August 2014, the paper cited the number of times a single article of more than 25,000 times.)

对于图像不同纹理特征值（能量、熵、惯性矩、相关）的提取(For different image texture feature value (energy, entropy, moment of inertia, related) extraction)